supervision of the air loop in the columbus module · abbreviations afs air mass ﬂow sensor. 12,...

Master of Science Thesis in Vehicular SystemsDepartment of Electrical Engineering, Linköping University, 2016

Supervision of the air loopin the Columbus Moduleof the International Space Station

Jasper Germeys

Master of Science Thesis in Vehicular Systems

Supervision of the air loop in the Columbus Moduleof the International Space Station

Jasper Germeys

LiTH-ISY-EX--16/5014--SE

Supervisor: Daniel Jungisy, Linköpings University

Examiner: Erik Friskisy, Linköpings University

Department of Vehicular SystemsDepartment of Electrical Engineering

Linköping UniversitySE-581 83 Linköping, Sweden

Copyright © 2016 Jasper Germeys

Abstract

Failure detection and isolation (FDI) is essential for reliable operations of com-plex autonomous systems or other systems where continuous observation or main-tenance thereof is either very costly or for any other reason not easily accessible.

Beneficial for the model based FDI is that there is no need for fault data to detectand isolate a fault in contrary to design by data clustering. However, it is limitedby the accuracy and complexity of the model used. As models grow more com-plex, or have multiple interconnections, problems with the traditional methodsfor FDI emerge.

The main objective of this thesis is to utilise the automated methodology pre-sented in [Svärd, 2012] to create a model based FDI system for the Columbus airloop. A small but crucial part of the life support on board the European spacelaboratory Columbus.

The process of creating a model based FDI, from creation of the model equations,validation thereof to the design of residuals, test quantities and evaluation logicis handled in this work. Although the latter parts only briefly which leaves roomfor future work.

This work indicate that the methodology presented is capable to create quite de-cent model based FDI systems even with poor sensor placement and limited in-formation of the actual design.

Carl Svärd. Methods for Automated Design of Fault Detection and Isolation Sys-tems with Automotive Applications. PhD thesis, Linköping University, VehicularSystems, The Institute of Technology, 2012

iii

Acknowledgements

This thesis has taken some time to finish and has defined quite a good portionof my being and by that does it not only mark the end of a great time but also apromise of future developments. This is a huge milestone for me and thus couldthese acknowledgements never fully contain the depth of my gratitudes.

First and foremost must I acknowledge Erik Frisk and the whole Vehicular Sys-tems department at Linköping university and Airbus DS for allowing me to workupon such an interesting system, even though it has given me plenty of grief attimes. Daniel Jung for always keep trusting in me but also Enrico Noack andMikael Persson for the time during the preparation work. Not to forget my oppo-nent Gustav Romeling.

Special thanks to Bengt Göransson, 千葉大奈, 李森 and 仇隽挺 for helping meto get back on track at a time where motivation was a great shortcoming, for thiswill you not be forgotten.

Alla mina vänner under studietiden, speciellt Björn Holm, Dan Adolfsson, FredrikHenriksen och Mikael Göransson samt familjerna LeMoine och Lockwood. Menäven Stefan Persson som lyckats stå ut med mig under så lång tid.

Carl Forden, Jonas Frossmed och Fritiof Olsson för simpel men pålitlig vänskap.Anna för alla dessa underbara konversationer som skapar glädje i vardagen.Familjen Asp för ett evigt familjeband samt David, Matte och Peter i Nässjö.

Joakim Råberg då även små människor kan göra stordåd.

The supporting friends in the Mahjong community like David Clarke, MatthiasKöhler, Senechal and Gemma Sakamoto among others, you know who you are.

Folket på Gata/Park i Nässjö kommun för er hjälp att komma ut ur arbetslöshetenslömska klor men även för er förståelse när jag valde att lämna er för högre utbild-ning, det tog sin tid, men nu är det klart och utan er hade detta inte skett.

Familjen Borg för deras outgrundliga vänlighet och stöd. Familjerna Hjelm, Kvist,Nylander samt Pieters i Kärrgruvan, Norberg samt familjen Högström för allt nibetytt för mig.

Solbritt Sundvall för sitt gedigna engagemang att försöka reda ut en karl av mig.

En natuurlijk mijn familie, die altijd voor mijn zaak blijft staan.Ik hou van jullie allemaal.

Linköping, December 22, 2016Jasper RK Germeys

v

Contents

1 Introduction 11.1 The Columbus air loop . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Columbus failure management system . . . . . . . . . . . . . . . . 21.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Model and data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.5 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6 Related research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.7 Expected results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.8 Outline of the report . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Model description 72.1 The ducts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.1 Mass conservation . . . . . . . . . . . . . . . . . . . . . . . . 92.1.2 Passive flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Fans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.1 Fan curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.2 Fluid work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2.3 Fan flow by power consumption . . . . . . . . . . . . . . . . 12

2.3 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.4 Other equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 Model parametrisation and validation 153.1 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 Model error measurement . . . . . . . . . . . . . . . . . . . . . . . 163.3 Validation of sensor redundancy . . . . . . . . . . . . . . . . . . . . 173.4 Validation of the fan curves . . . . . . . . . . . . . . . . . . . . . . 203.5 Model parametrisation and validation . . . . . . . . . . . . . . . . 223.6 Validation of fan flow by power consumption . . . . . . . . . . . . 25

4 Design of fault detection system 274.1 The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2 Generating the MSOs . . . . . . . . . . . . . . . . . . . . . . . . . . 284.3 Selecting the residuals . . . . . . . . . . . . . . . . . . . . . . . . . 29

vii

viii Contents

4.3.1 System modes . . . . . . . . . . . . . . . . . . . . . . . . . . 294.4 Generating the residual code . . . . . . . . . . . . . . . . . . . . . . 304.5 Validation of the residuals . . . . . . . . . . . . . . . . . . . . . . . 32

5 Results 355.1 Fault detection and isolation with injected faults . . . . . . . . . . 355.2 Detection and isolation on real data . . . . . . . . . . . . . . . . . . 37

6 Conclusion 456.1 Overall evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Bibliography 49

Abbreviations

AFS air mass flow sensor. 12, 16, 18, 22, 23, 25

Airbus DS Airbus Defence and Space. 1

Airbus SE Airbus Group SE. 1

CFA cabin fan. 8, 15, 19, 25, 32

CHX condensate heat exchanger. 8, 22–24

CHXFA condensate heat exchanger fan. 12

COL-CC COLumbus Control Centre. 2

COL-DMS COLumbus Data Management System. 2

CTCU cabin temperature control unit. 8, 13, 20

CUSUM cumulative sum. 5, 35

CWSA condensate water separator assembly. 13, 16

DAG directed acyclic graph. 30, 31

EADS European Aeronautic Defence and Space company NV. 1

EU electronic unit. 10

FDI failure detection and isolation. 3–5, 45–47

GLR generalized likelihood ratio. 5

HEPA high-efficiency particulate arrestance. 1, 8, 40

HS humidity sensor. 13, 19, 20

ix

x Units

IMV inter module ventilation. v, vi, 8, 10

IRFA inter module ventilation (IMV) return fan. 8, 10, 15, 19, 25

ISFA IMV supply fan. 8, 15, 19, 22, 23, 25

ISS International Space Station. 1

KH total dynamic head. 11, 24

LCOS liquid carry over sensor. 13

MAE% mean absolute error percentage. 16, 18–20, 23–25

MSE mean square error. 16, 18–20, 23–25

MSO minimal structurally overdetermined set. 5, 27–30, 46

NaN not-a-number. 32, 35

SCC strongly connected components. 27

TPS air pressure sensor. 13, 17

Units

I Current [A]N Revolutions per minute [min−1]Q Volume flow [m3/s] most of the time in [m3/h]R Gas constant [J/kgK]T Temperature [oC]W Watt [W ]φ Relative Humidity [-]ρ Density [kg/m3]p Pressure [kP a]q Mass flow [kg/s] exclusively used as [kg/h] in this document

1Introduction

The European space laboratory Columbus is designed and built by EuropeanAeronautic Defence and Space company NV (EADS), now Airbus Group SE (Air-bus SE), affiliate Astrium, now Airbus Defence and Space (Airbus DS) [AirbusDefense and Space, 2013a], and is a part of the International Space Station (ISS).It is equipped with a range of experimental facilities and basic life support forup to three astronauts. Together with the microgravity environment, Columbushas enabled numeral extraordinary experiments previously not possible in manyscientific fields such as physics, material science, biology, medicine, and humanphysiology [Airbus Defense and Space, 2013b,c].

1.1 The Columbus air loop

The Columbus air loop main function is to provide a forced air flow to avoid deadair pockets which is safety critical for the crew. The forced air flow also enablesfire detection and removes heat from air cooled equipment. As the ColumbusModule has no O2 or CO2 control, the inter module ventilation has to providefresh air to support life on board.

The air loop consists of four fans, three with check valves, one without, a high-efficiency particulate arrestance (HEPA) filter and a condensate heat exchanger.A simple overview of the system is shown in Figure 1.1. The system is designedto be operating in 8 stable modes and additionally 43 interim modes intended forair loop reconfiguration.

1

2 1 Introduction

Figure 1.1: A schematic overview of the Columbus air loop, [Source: AirbusDS, 2013]

1.2 Columbus failure management system

The failure management system on Columbus consist of multiple parts; First isthe on board COLumbus Data Management System (COL-DMS) that collects andlogs all measured signals. Being fully automatic it can quickly initiate requiredresponses but due to sparse resources, the diagnosis part of COL-DMS is limitedto only detect time critical failures like fire outbreaks and other environmentalhazards.

The second part is the COLumbus Control Centre (COL-CC) which is the primaryground based unit. It receives data from COL-DMS in near realtime througha downlink and performs data analysis, both manually and automatically. Thefailure management part of COL-CC has a similar purpose as the onboard unitand focuses primarily on detection of critical failures and short term responses.This is supported by an Engineering Support Centre that does offline analyses ofselected data to find and direct long term corrective actions for COL-CC and anAssembly, Integration and Test facility for testing and development purposes.

An onboard monitoring experiment, the ERNO BOX, has been added as a thirdpart to the failure detection arsenal. This system runs in parallel to COL-DMS,monitoring the same data but with no means for interaction. Instead the re-sponses are submitted to COL-CC for comparison with the decisions made byCOL-DMS [Noack et al., 2012].

There has been some recent developments in the COL-CC infrastructure too im-prove the failure management efficiency [Sabath et al., 2012, 2014].

1.3 Background 3

1.3 Background

Since launch in February 2008, Columbus has encountered multiple failures andabnormalities. The failures have either been minor or been recovered before anyserious damage to the system has occurred. It has emerged that the original fail-ure detection system has weak robustness and poor fault sensitivity [Noack et al.,2010].

Upon request from Astrium, European universities have been engaged in devel-opment and improvement of the diagnosis system on Columbus. BTU Cottbusis using a data mining approach and Linköping University by means of modelbased failure detection. [Noack et al., 2011, 2012, Noack and Schmitt, 2012]

Beneficial for the model based failure detection and isolation (FDI) is that thereis no need for fault data to detect and isolate a fault in contrary to data cluster-ing. However, it is limited by the accuracy and complexity of the model used. Asmodels grow more complex, or have multiple interconnections, problems withthe traditional methods for FDI emerge. In [Svärd, 2012] an automated method-ology is presented for design of an FDI system for complex systems operating inmultiple modes.

This thesis focuses on the design of a model based FDI system for the Columbusair loop. A small but critical part of the Columbus life support.

300

350

400

450

500

AirFlow Sensors [m3/min]

AFS1 AFS2

50

100

150

200

Pressure Differences [Pa]

CHX cfa1KH cfa2KH isfaKH filtered CHX

0 500 1000 1500 2000

0.8

1

1.2

Fan rotation [rpm]

cfa1N cfa2N isfaN irfaN

Figure 1.2: Subsampled unproccessed data

4 1 Introduction

1.4 Model and data

A model of the Columbus air loop was proposed during the summer of 2012by Jasper Germeys and Mikael Persson. This model, which is based on physicalrelations and measured data from the time span of 2008 to 2010 provided byAstrium, has been used as basis for the model in this thesis. A second batch ofdata containing a more detailed selection of all the more interesting parts fromthe initial set was later received.

In this thesis only the second data set and an additional third data set, where thefans’ rotation are increasing in steps, are used. This third data set has been usedas training data for the parameterisation of the model. A selection of the signalsin the second and third data sets are displayed in Figure 1.2 and Figure 3.1 respec-tively. In Tables 2.1 and 2.2 the sensors contained in the data sets are listed. Ad-ditionally, Figure 1.1 and Figures 2.1-2.4 are taken from these documentations.

1.5 Problem formulation

The purpose of this thesis is to develop a diagnosis system that will not onlydetect but isolate the failures included in the model. The system suffers fromnoisy sensors and fault propagation, hence will simple means for fault isolationnot suffice in aspect to minimise the risk of false alarms. This includes detectionof the fans’ current work mode and determine actual mass flow as it is this centralstate that has poor observability. This will pose a problem in unknown modes andwhen clogging or leaks occur as those faults are highly linked.

The FDI system should;

• Be designed based on a model of the air loop.

• Be able to detect which mode the fans operate in.

• Detect and isolate single faults such as fan loss, leaks, wear, (partial) clog-ging, and sensor loss.

1.6 Related research

A model-based design of a diagnosis system can be divided into three phases;A modelling phase, a residual generation phase and finally an evaluation phasewhere the rules for fault detection and isolation are set. These phases entwineand will be done iteratively.

In the modelling phase the full model equation set will be extended with thedesired detectable faults and modes. Structural fault detectability and isolabilitywill be analysed by using the Dulmage-Mendelsohn decomposition [Frisk et al.,2012]. The parameters of the equations will then be estimated from sample datawith no faults.

1.7 Expected results 5

The residual generation phase consist of extracting the minimal structurally over-determined set (MSO) to generate the residuals [Krysander et al., 2008]. As it isunknown how many alternative sets the model allows, initially a greedy selectionalgorithm described in [Svärd et al., 2013] will be applied.

The evaluation phase consist of creating the rules for detection and isolation.There are many methods available for detection, where constant thresholds be-ing the simplest, but also more robust methods as cumulative sum (CUSUM),likelihood functions, adaptive thresholds [Sneider and Frank, 1996], or combina-tions thereof [Meinguet et al., 2012] have proven effective. As the system requiresrobustness and will be operating in multiple modes, a relaxed generalized likeli-hood ratio (GLR), as proposed in [Svärd et al., 2014], was to be considered.

1.7 Expected results

The expected results of this work is an FDI system that detects and isolates faultsin the air loop system. The system should be able to work in real time and detectfaults within reasonable time. It is also expected that the system could measurewear on the fans and partial clogging.

An evaluation shall be done if the methodology presented in [Svärd, 2012] is aworking methodology for this system or not. Also an analysis if the solution canbe improved by using Kullback-Leiber divergence for fault isolation as suggestedby [Eriksson et al., 2011].

Additionally an evaluation on how well the FDI system works in comparison tothe uncertainties in the model and if simpler methods could suffice.

1.8 Outline of the report

The plan for the report outline is as following;Chapter 1 introduces the problem and the system, the chosen methodology andrelated research as well as the expected results and this outline of the rest of thereport.

In Chapter 2 the details on the model and the equations sets are listed. Thework of setting the model parameters are presented in Chapter 3 together withan estimation of the expected noise while in no fault node. Chapters 2 and 3 willtogether form the modelling phase of the work.

Chapter 4 displays the full model including modes and faults and continues tothe residual generation and what sets the methodology generates. These are thenevaluated and the result are presented. How well the design works against col-lected data is presented in Chapter 5.

Chapter 6 contains the evaluation of the work, made together with ideas for po-tential future work.

2Model description

The Columbus air loop consist of a small set of flows with some sensors and fans.This chapter will describe these compontents and the models used to representthem. A 3D model of the air loop has been provided by Astrium which is shownin Figure 2.1. Even though the 3D model is trusted to be an accurate representa-tion of the air loop, no detailed data has been gathered from this figure.

Figure 2.1: A 3D model of the Columbus air loop, [Source: Airbus DS, 2013]

7

8 2 Model description

2.1 The ducts

The ducts have been split into the different regions based on the air flow theyare expected to contain for modelling purposes as shown in Figure 2.2. Belowfollows a description of the different sections. The actual dimensions of the ductsare not known but it can be fair to assume they all are static.

Harmony module: The module supplying Columbus with air and receiving thereturned air. This module is not considered a part of this system and is thusnot modelled. We can not assume there are any correlation between the twoflows from this section due to unknown internal structure.

supply: Transfers air from the Harmony module. This region has many un-knowns as the only sensors are those from the inter module ventilation(IMV) supply fan (ISFA). The input flow may have any temperature or hu-midity even if it is assumed to be constant during normal operation. Thereis both a check valve and a manually controllable valve.

outlet: From the junction of the supply and fan{1,2} to the multiple outlets to thecabin. In this section the air gets filtered through a HEPA filter, regulatedin a controllable cabin temperature control unit (CTCU) with a condensateheat exchanger (CHX) and finally distributed to the cabin. Apart from thecabin this is the only section where the properties of the air is expected tochange. The pressure loss is expected to be quite high. There is a sensormeasuring the pressure difference over the heat exchanger. It is unknownif the water purging system is contained between this sensor or not.

fanJ: Small region where both cabin fans share the ducts, it is unknown if themajority of this section is before or after the actual fans but should not berelevant for modelling purposes as it should only contain the air flow thatis produced by the cabin fans.

fan{1,2}: Small region where only the cabin fan{1,2} (CFA) affects the flow, draw-ing air from the inlet region to the supply and outlet junction. Both fanshave check valves and should virtually be identical. These sections are sub-sections of the fanJ section.

inlet: Small region that is transferring air from the cabin to the fanJ and returnsection. There should be an air filter that can be clogged here. There is alsosmoke detectors mounted in this section.

return: The section where air returns to the Harmony module. Just as for thesupply section, only the sensors provided by the IMV return fan (IRFA) areknown, however, this section should not affect the system that much on awhole. There is no check valve in this section but there is a controllablevalve.

cabin: The actual cabin. Some of the sensors, like pressure and cabin tempera-ture, should be mounted in this section. This section is not a duct and can

2.1 The ducts 9

not be considered sealed. It is however, fair to assume that there is somecorrelation between the in and out flow from this section.

supply outlet

fanJ

inletreturn

Har

mon

ym

odu

le

cabin

Figure 2.2: A schematic overview of the ducts and air flows

2.1.1 Mass conservation

Under the assumption that there are no leaks in the system, the local pressurewill variate based on the mass flow in and out of each node as a function of inand out flow.

p = f (Qin, Qout) (2.1)

Additionally, it is assumed that the local pressures in the system are constant inthe time frame giving the following relations between the flows:

Qoutlet =Qsupply + Qf anJ (2.2a)

Qf anJ =Qf an1 + Qf an2 (2.2b)

Qinlet =Qreturn + Qf anJ (2.2c)

The sections are described in Section2.1.

2.1.2 Passive flow

Sections without a fan will have a flow driven by the pressure difference. This canbe modelled by using the Bernoulli equation but as there are too many unknownsin the system, as duct size, number of bends and if the flow is laminar or not, asimplified model is used instead given by

∆pi = Kf ,i(QiKc,i − Kd,i)2 (2.3)

which translate to that most terms in Bernoullis equation are considered constant,neglectable or flow dependant.


2.2 Fans

Each fan has a certain number of sensors as listed in Table 2.1. All four fans are ofthe same type with an attached electronic unit (EU). Thus, one model structureshould apply to them all. There is a check valve connected to the fan output toprevent reverse flow for all fans, except the IMV return fan. A schematic overviewof a fan is displayed in Figure 2.3.

Figure 2.3: A schematic overview of a fan. [Source: Airbus DS, 2013]

Table 2.1: Fan signals

Measurementspart signal unit

Delta_P_ kP aFan_Speed_ min−1

CFA{1,2}_/ Input_Current_ AI{S,R}FA_ EU_Temp_ oC

Fan_Temp_ oCInput_Voltage_ VSwitches/Analyses

CFA{1,2}_/ Pwr_Stat_ ON/OFFI{S,R}FA_ Avail_Stat_ (UN)AVAILCFA_ Redun_Stat_ (UN)AVAIL

I{S,R}SOV_Vlv_Open_Stat_ X/OPENVlv_Closed_Stat_ X/CLOSED

2.2 Fans 11

2.2.1 Fan curve

It is common practice to utilise fan curves when designing ventilation systems toensure that the fans are working in their efficient load regions by calculating thedifferent operation points. Figure 2.4 shows the fan curve for the current fans.This curve is normally generated in ideal (dry) conditions and to scale this forother fan speeds (N ) and air densities (ρ) the affinity equations

dp1

dp2=ρ1

ρ2

(N1

N2

)2

,Q1

Q2=

(N1

N2

),

Wf 1

Wf 2=ρ1

ρ2

(N1

N2

)3

(2.4)

are utilised. A map has been made to calculate flow (Q) as a function of totaldynamic head (KH) (dp), i.e., the fan’s workload. However, there are two possi-ble flows, and the fluid effect (Wf ) the fan can generate depends on where onthe curve the fan is operating as illustrated in Figure 2.4. The lower flow out-put, the so called stall region, is considered a fault as it is not only less efficientbut also induces more wear on the fan. There are multiple reasons for a fan toenter stall mode where the most common ones are from the design stage like over-dimensioning of the fans or competing parallel flows. It is assumed that this fancurve is valid for 8500 rpm and with an air density of 1.2041 kg/m3 (dry air at20oC).

Figure 2.4: Characteristics of the fans, [Source: Airbus DS, 2013]


2.2.2 Fluid work

All attempts to utilise the fan curve to determine an operational point by mea-sured power consumption has been very unreliable. The system loss is not in-significant. Analysis indicates that the effect in the fan curve is measured (Wm)and not fluid effect (Wf ), this implies the scaling law for W does not apply di-rectly. This observation is strongly supported by data as well.

Wm = Wf + Wloss

To determine Wloss in the ideal conditions is not feasible and there is no basis toassume Wloss would scale as nicely. Instead, a model which is given by

W = Kp∆pQ + KqQ2 + Kd (2.5)

has been estimated from the fan curves map by linear regression where Kp wouldbe the fan’s efficiency in building pressure, Kq flow work efficiency and Kd anyconstant losses. This approach is accurate enough to determine if the fan is work-ing in the desired mode or in the faulty stall mode. This is later validated inSection3.4.

2.2.3 Fan flow by power consumption

An already parameterised model has been received from Astrium which is a di-rect correlation between the fan power consumption and the flow that the fansproduce. This model is done by linear regression of collected and, possibly, pre-processed data.

W = Kf Q + Kd (2.6)

The validation in Section3.6 conclude that this model is accurate within certainintervals which should be enough for residual generation.

2.3 Sensors

A complete listing of the available sensors is shown in Table 2.1 together withTable 2.2. Some of these sensors have been concluded to be of no use to thecurrent model and are consequently not included.

AFS : The air mass flow sensors, positioned in the junction between supply,fan{1,2} and outlet. Will be abbreviated as AFS{1,2} in most parts of thisdocument. The sensors are validated in Section3.3 but are often saturatedand due to local vertices does rarely produce redundancy. An improvedmodel (2.7) has been made in combination with (2.2a) which is validated inSection3.5.

AFSi =∑

Kj,iQj + K0 (2.7)

CHXFA : The pressure sensors over the condensate heat exchanger fan. Arein used in combination with (2.3) to produce an estimation of Qoutlet , val-

2.3 Sensors 13

idated in Section 3.5 and directly in (3.2) which also is validated in Sec-tion3.5.

CTCU : Multiple sensors and switches, only the cabin temperature signals areused in the current model and these are validated in Section3.3.

CWSA : Condensate water separator assembly. None of these signals are usedin the current model.

HS : Air humidity sensor, measuring percentual water saturation in the air,used to calculate current air density (2.11) and is validated in Section3.3.

TPS : Air pressure sensor, measured in mmHg and used in the system and isvalidated in Section3.3. Will be abbreviated as TPS(1-4).

LCOS : Liquid carry over sensor. Used to detect if the CWSA fails which isoutside the scope of this thesis.

Cabin : Smoke detectors and a low airflow state, none of these are currently inuse by this model.

Table 2.2: Non fan signals

Measurementspart signal unitAFS{1,2}_ Cab_Air_Massflow_ kg/hCHXFA_ Delta_P{1,2}_ P a

CTCU{1,2}_Cabin_Temp{1-3}_ oCAvg_Cabin_Temp_ oCTCV_Posn_ %

CWSA{1,2}_Input_Current_ ADelta_P_Air_ kP aDelta_P_Water_ kP a

HS{1,2}_ Air_Humidity_ %rHTPS{1-4}_ Air_Press_ mmHgLCOS{1,2}_ Level_ V

Cabin_SD{1,2}_Obscuration_ VSD{1,2}_Scatter_ V

Switches/Analyses

CTCU{1,2}_

Pwr_Stat_ ON/OFFKick_Posn_Up_Stat_ (IN)ACTIVEKick_Posn_Down_Stat_ (IN)ACTIVEKick_TCV_Hold_Posn_ (IN)ACTIVEDryout_Stat_ DISABLEDCntl_Loop_Stat_ (DIS)ABLED

CWSA{1,2}_ Pwr_Stat_ ON/OFFCabin_ Low_Airflow_Stat_ NOM/LOW


2.4 Other equations

There are some other equations used in the model that are of a more generic type,like the pressure difference

∆p = pa − pb (2.8)

and conversion from mass flow (q) to volume flow (Q)

q = ρQ (2.9)

Ohm’s Law for calculating power

W = IV (2.10)

used to calculate the fans’ power consumption where W equals Watt, I the sup-plied current and V the voltage.

To calculate the air density (ρ) as a function of pressure (p), temperature (T ) andrelative humidity (φ) the ideal gas law is used

ρ = f (T , p, φ) (2.11)

ρ =pairRairT

+pwaterRwaterT

, pwater = psatφ, pair = p − pwater

where px are partial pressures and Rx being the specific gas constants.

3Model parametrisation and validation

This chapter contains verification and parametrisation of the models listed inChapter 2. Starting with an overview of the data sets used throughout the chap-ter and a description of how the model error is measured. This is followed byverification of the system redundancy and the fan curve assumptions. Finally theparametric models are both estimated and verified.

3.1 Data sets

For parametrisation of the models, a data set where the fans are being tested indifferent modes has been used. This training data is not normal operation butmade upon request for assisting in modelling the system. For validation somedata sets with fairly normal operation has been selected and a data set which is asubsampled version of all available data not used as training data. Below followsa short description of each data set and an overview of the training data set isshown in Figure 3.1.

Training data: Data set from 20100[1-2]* which is a testing run to gather infor-mation of how the system change in the different modes. Both IRFA andISFA is enabled with open valves. The temperature, cabin pressure andhumidity is either constant or within normal variation.

n20080319: Validation data set with low sample rate on some signals, CFA2 isdoing the cabin flow and all fan modes and valves are kept at a constantrate.

n2008070: Validation data set going from normal operation to internal modewhere the supply fan gets disabled and both cabin fans are run at high

15

16 3 Model parametrisation and validation

300

400

500

Airflow Sensors [kg/h]

AFS1 AFS2

50

100

150

200

Pressure Differences [Pa]

CHX cfa1KH cfa2KH isfaKH filtered CHX

0 500 1000 1500 2000

0.8

1

1.2

Fan rotation [rpm]

cfa1N cfa2N isfaN irfaN

Figure 3.1: Raw sensor data from the training data set

speed. The return fan is disabled the whole set but its valve is opened inthe internal mode. The supply valve is not closed. There is a slight peak inhumidity when the valves close.

20091129: Validation data set in normal operation with a swap of which cabinfan is running, followed by the return fan disabled and its valve closes.There is also noticeable drops in the AFS sensors due to the CWSA work-ing.

subsampled data: All available data is subsampled for quicker processing andused for validation. The last registered value is used when data is missing.

3.2 Model error measurement

To measure the model error, the model equations are evaluated on the validationdata sets. Mean square error (MSE) and mean absolute error percentage (MAE%)have been used to determine if an equation seem to hold true as

MSE :1N

N∑i=1

(xi − xi)2 MAE% :1

100N

N∑i=1

|xi − xi |xi

(3.1)

in addition to a manual behavioural check.

3.3 Validation of sensor redundancy 17

3.3 Validation of sensor redundancy

Validation of the simplest form of redundancy where multiple sensors ought tomonitor the same information. This is done to determine if the sensors truly areredundant and, if not, how much their output differs. As there is no need forestimation in this section, the training data set is also used as validation data.

Air pressure sensors

The air pressure sensors are quantified to steps of 0.4mmHg. The residual endsup being within one, occasionally two, quantification steps after adjusting offset.If this is due to constant local pressure differences or due to bad calibration can-not be determined. The only contradiction to this can be found in the subsampleddata set as shown in Figure 3.2. The biggest offset is between TPS1 and TPS4 of9 steps being 0.48kP a (3.6mmHg).

0 500 1000 1500 2000730

740

750

760

770

780Raw signals in mmHg

TPS1 TPS2 TPS3 TPS4

0 500 1000 1500 2000

-5

0

5

Residuals after removing offset

TPS1-TPS2

0 500 1000 1500 2000-1

0

1

TPS1-TPS3

0 500 1000 1500 2000

-5

0

5

TPS1-TPS4

max: |6.092|

Figure 3.2: Validation of the air pressure sensors on the subsampled data set


Air flow sensors

At the junction with the air mass flow sensors (AFS) there are multiple modelsthat could be used to determine the actual flow in the region. As shown in Fig-ure 3.1 the AFS sensors do not give a good indication of the true flow in theregion. In Table 3.1, the results are listed together with how many percent of thedata is excluded due to saturated sensor. The difference between the sensors isoften bigger when the sensor is capped.

Table 3.1: Air mass flow sensors redundancy error

data MSE MAE% Capped%training 3847.4 11.42 33.5n20080319 1088.7 8.22 0.0n2008070 214.78 1.79 70.7n20091129 182.27 2.48 21.1subsampled 4407.9 11.29 32.3

Cabin fan pressure sensors

It is only when both fans are enabled at the same time that we can utilize thisredundancy. It is not known if this is never recorded or if it is discarded. Amongthe selected validation data set there is only redundancy in the n2008070 data setand the subsampled data set. There seems to be an offset between these and wecan assume part of it originates from the differences in duct design. However itdoes not seem to be constant which could be due to clogging in the system. In then2008070 data set the predicted offset is as large as 10%. The MAE% is 5.34% inthe subsampled data set while 3.79% in the n2008070 data set.

0 500 1000 1500 2000

-0.1

-0.05

0

0.05

0.1n2008070

0 50 100 150 200 250 300 350-0.3

-0.2

-0.1

0

subsampled

17

-Fe

b-2

00

8

09-Jul-2008 03-Aug-2009

max: |0.07685|

max: |0.2943|

Figure 3.3: Residuals of the cabin fan pressure sensors

At the beginning of the 3rd August 2009 section of the subsampled residual, asshown in Figure 3.3, there is a clear disturbance that is believed to originate fromthat the just starting fan is having problems leaving stall mode. During thisinterval both fans are set to run at high speed while the supply fan was abruptly

3.3 Validation of sensor redundancy 19

disabled indicating that there was something wrong with the supply and a desireto vent out the module was present. However, it is believed that this made one ofthe fans not able to ensure a positive flow.

Fan input voltage

The input voltage to the fans are often varying and the signal is depending onthe modes the fan is working in. This variation is fairly small and the variationis normally within one percent. In Table 3.2 the MSE and MAE% of the differentfans are shown when comparing to the supply fan.

Table 3.2: Fan voltage redundancy error

ISFA vs IRFA CFA1 CFA2data MSE MAE% MSE MAE% MSE MAE%training 0.964 0.79 0.191 0.34 0.859 0.74n20080319* 2.189 1.20 - - 0.953 0.79n2008070* - - 0.044 0.17 - -n20091129* 1.141 0.86 0.134 0.30 0.978 0.80subsampled 1.945 1.12 0.094 0.24 0.963 0.79

Humidity sensors

As shown in Figure 3.4 are the humidity sensors displaying slightly different dy-namics indicating that they must have different positions in the system. The MSEand MAE% are displayed in Table 3.3.

0 500 1000 1500 200040

41

42

43

44

45

46

47Air Humidity in %

HS1 HS2

0 500 1000 1500 2000-2

0

2

HS1-HS2

max: |1.905|

Figure 3.4: Humidity sensors from the training data set


Table 3.3: Humidity sensors redundancy error

data MSE MAE%training 1865.67 5.94n20080319 1849.63 5.17n2008070 1178.48 2.16n20091129 1848.76 3.76subsampled 1641.13 4.87

Temperature sensors

When comparing the temperature sensors, it is concluded that they stay within aquantification step between each other. The variation of the average temperaturesignal indicates that it is calculated from more detailed data. CTCU2 is most ofthe time not in use at all. No offset or drifting has been detected.

0 500 1000 1500 200022.5

23

23.5

24Raw signals in

oC

Temp1 Temp2 Temp3 Avg

0 500 1000 1500 2000-0.5

0

0.5Residuals

Temp1 - Temp2

0 500 1000 1500 2000-0.5

0

0.5

Temp1 - Temp3

0 500 1000 1500 2000-0.5

0

0.5

Avg - mean(Temp(1-3))

Figure 3.5: Validation of the CTCU1 temperature sensors

3.4 Validation of the fan curves

As no true flow is known in the system, it is assumed that the fan curve togetherwith the affinity laws generate a flow that could be utilised instead. The equationsdisplayed in Section 2.2.1 are part of the affinity laws and will not be validated.Since the estimations involving flows produced by this method yield better re-sults than the other methods tested indicate that the method is functional.

3.4 Validation of the fan curves 21

Detection of fan modes

To utilise the fan curves the fan mode must be determined. Initially this was donemanually on the training data set to generate estimations but as the generalisa-tion, described in Section2.2.2, produced an overall better result, all estimationswere re-generated using this. A comparison of the manual and automated modedetection is shown in Figure 3.6.

0 500 1000 1500 2000

200

300

400

500

600Airflow [kg/h]

CFAX:High, ISFA:High CFAX:Low, ISFA:High CFAX:High, ISFA:Low AFS1 AFS2 filtered Estimation

0 500 1000 1500 2000 low

high

lowISFA mode

automatic manual

0 500 1000 1500 2000 low

high

lowCFA1 mode

automatic manual

0 500 1000 1500 2000 low

high

lowCFA2 mode

automatic manual

Figure 3.6: Estimation of modes

Decoupling of air density

Early in the construction of the model it was realised that the air density depen-dency often was not properly defined among the equations and input signals. Inaddition it was desired to decouple the density from the fan curves. The conclu-sions however, was that the fan curve and mode detection could not be satisfac-tory decoupled from the air density on a modelling basis. As fan mode is requiredto determine the produced flow, air density has not been decoupled from the fancurve model.


3.5 Model parametrisation and validation

The model is parametrised using linear regression in the case of (2.7) and (3.2)and curve fitting (Levenberg-Marquardt) in the case of (2.3). The parametrisationis done using the whole training data set except in the case of (2.7) where thesamples with capped AFS signals were discarded.

AFS i − Q : AFSi =∑

Kj,iQj + K0 (2.7)

CHXdp − Q : ∆pi = Kf ,i(QiKc,i − Kd,i)2 (2.3)

kh − CHX : kh = Kv∆p + K0 (3.2)

AFS-Q equation

A complete overview of the parametrisation of (2.7) is shown in Table 3.4. Overallyields the AFS2 sensor a very good result while the AFS1 parametrisation appearsto be clearly faulty. An observation of the estimated parameters show that AFS2uses about 60% of ISFA and 30% of the cabin fans. While the AFS1 parametri-sation uses 30% of them all. An re-parametrisation weighting ISFA higher forAFS1 might give better results, but as the AFS1 sensor is often saturated, thismight never be possible.

0 500 1000 1500 2000 25000

200

400

AFS1-Q [m3/h]

AFS1 Estimate AFS1 abs(error)

0 500 1000 1500 2000 25000

200

400

AFS2 Estimate AFS2 abs(error)

0 500 1000 1500 2000 2500 low

highCFA1 mode

0 500 1000 1500 2000 2500 low

highCFA2 mode

max: 160.7

max: 117.3

Figure 3.7: Validation of the AFS-Q parametrisation (n2008070)

In the n2008070 data set, displayed in Figure 3.7, the AFS2 parametrisationturned worse when the ISFA valve is opened. This could indicate that eitherthe ISFA check valve is not operational or that there is a passive flow from the

3.5 Model parametrisation and validation 23

supply module. This behaviour can also be seen in the n20091129 data set whenthe ISFA valve is closed.

Table 3.4: AFS-Q parametrisation error

data AFS1 MSE AFS1 MAE% AFS2 MSE AFS2 MAE%training 12689 27.14 385.4 4.09n20080319 4001 18.27 34.2 1.15n2008070 14738 28.62 806.9 5.38n20091129 15400 29.91 419.1 5.04subsampled 13915 28.43 576.2 5.09

CHX-Q equation

It can clearly be seen that the parametrisation of (2.3) is not perfect when viewingits match to the training data set but seems to be holding quite well overall evenif it appears to be suffering from an offset. It has to be analysed if this is driftingand thereby could be caused by clogging or any similar fault. In Figure 3.8 can theworst case of offset among the selected validation data sets be seen. Interesting ishow the offset change sign when the working cabin fan is changed about sample1700. Additionally the fan is having problems leaving the faulty stall mode whichit finally manages once the return fan gets disabled just before sample 2000.

Table 3.5: CHX-Q parametrisation error

data MSE MAE% MSE filtered MAE% filteredtraining 246.0 11.03 151.7 8.90n20080319 228.5 12.45 58.9 6.08n2008070 207.0 10.80 9.5 2.20n20091129 156.0 9.62 52.5 5.71subsampled 218.1 16.23 69.0 6.47

0 500 1000 1500 200040

60

80

100

120

140CHX-Q [Pa]

CHX Estimate filtered CHX filtered Est

0 500 1000 1500 20000

20

40

60

80

abs(error) abs(filtered error)

max: 47.36

max: 13.72

Figure 3.8: Validation of the CHX-Q parametrisation (n20091129)


KH-CHX equation

The results of the parametrisation of (3.2) is shown in Table 3.6. It can be notedthat the parametrisation seem to overshoot when the return valve is closed. Thishappens in the first part of the n2008070 data set as seen in Figure 3.9 and in thelatter part of n20091129 data set. This could indicate a back flow through thereturn fan or that there is a forced exhaust flow even when the fan is disabled.

Table 3.6: KH-CHX parametrisation error

data MSE MAE% MSE filtered MAE% filteredtraining 0.012 10.41 0.0089 8.53n20080319 0.011 10.36 0.0022 4.00n2008070 0.023 16.27 0.013 14.19n20091129 0.014 11.81 0.0084 10.36subsampled 0.018 13.85 0.010 9.49

0 500 1000 1500 20000.2

0.4

0.6

0.8

1

1.2KH-CHX [kPa]

Estimation cfaXkh filtered Est

0 500 1000 1500 20000

0.2

0.4

0.6 abs(error) abs(filtered error)

max: 0.5292

max: 0.1961

Figure 3.9: Validation of KH-CHX parameterisation (n2008070)

3.6 Validation of fan flow by power consumption 25

3.6 Validation of fan flow by power consumption

This model (2.6) has been verified against both the air flow sensors (AFS againstf (W )) as displayed in Table 3.7 and against the air flow generated from the fancurves (W against f (Q)) (2.4) as shown in Table 3.8 with varying results. Themodel seems to hold fairly well when the fans operate in certain regions, butas this is quite often not the case the usability might be limited. Note that allfans are identical while the fan model (2.6) has different parameters for eachfan. This could be a case of parametrisation for that fan’s default working regionrather than an overall. When using the parameters for CFA2 on the CFA1 flowan MAE% improvement of about 10% is achieved. Doing a similar attempt forIRFA does not yield any improved results, however as the IRFA flow is unknownthis method can not be determined incorrect.

Table 3.7: AFS estimation by power consumption error

data AFS1 MSE AFS1 MAE% AFS2 MSE AFS2 MAE%training 57562 37.9 56518 37.2n20080319 4288.7 16.2 1095.0 7.6n2008070 3360.6 10.8 3377.9 11.6n20091129 2782.3 9.6 569.6 4.9subsampled 6730.6 15.6 3361.3 8.1

Table 3.8: Power consumption estimation by fan flow error

ISFA IRFA CFA1 CFA2data MSE MAE% MSE MAE% MSE MAE% MSE MAE%training 940.2 20.9 3645.9 58.9 1092.2 28.0 1810.0 18.7n20080319 3.3 1.2 2891.0 40.7 - - 37.4 6.5n2008070 14.0 2.6 - - 432.6 19.7 523.1 17.7n20091129 22.2 3.7 5.6 1.3 928.9 33.7 10.5 3.6subsampled 14.0 2.6 1684.0 25.5 1161.7 32.4 61.2 6.9

4Design of fault detection system

This chapter will explain how the faults are implemented, how the residualsare selected and finally tested. The desired detectable faults are added to themodel where it then is partitioned into minimal structurally overdetermined sets(MSOs) by using Dulmage-Mendelsohn decomposition analysing the stronglyconnected components (SCC) [Frisk et al., 2012, Krysander et al., 2008]. Onlywhen this is done is the greedy approach used to select the residuals.

4.1 The model

The complete model has 78 equations generating 55 states from 36 input signals.32 errors are handled and below follows a description on how these are includedin the modelled. The whole system is displayed in Figure 4.1.

Sensor Faults

Sensor faults (Fs) have been added into the model as an additive parameter to themeasured sensor (Ss) as

St = Ss + FsAs the original signal is known during simulation can multiplicative faults alsobe simulated by letting Fs be a function of Ss. This is added to all input signalsexcept the enabled and open/closed valve signals.

Duct clogging

A simulation of the complete model is required to determine how clogging reallyaffects the system. This is however not done and a heavily simplified fault is in-

27

28 4 Design of fault detection system

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stead simulated that could be seen as clogging. The fault is added as a parameticfault to (2.3) as additional duct flow resistance.

∆pi = Kf ,i(Qi(Kc,i + Fc,i) − Kd,i)2

Air leaks

Like with the clogging, a complete simulation is required to truly know how thistype of fault is affecting the system. The simplified method here is that the nega-tive fault (Fq) is added to the flows in (2.2) as

Qout = Qin + Fq

simulating that air has taken route elsewhere than the monitored connections.

4.2 Generating the MSOs

Finding the MSOs for a system this size is a very heavy task and has thereforebeen split into simpler sub tasks. For the chosen method the system is first sortedusing Dulmage-Mendelsohn decomposition as shown in Figure 4.1. An uppersubset, A, is arbitrarily selected that is considered fast enough to be completelysearched while still having overdetermined components. After which the rest, B,of the system is detached. This detached subsystem is searched for all existing

4.3 Selecting the residuals 29

MSOs and for each found MSO the dependant equations and states from A isadded to form a new matrix C that is again searched for all MSOs.

Consider the system shown in Figure 4.2. After selecting an A, which has at leastan overdetermined part (e5 and e6) the rest B is selected. Analysis of B yieldstwo MSOs that forms C1 and C2 as illustrated. Observe that for C2 has e2 beencompletely removed from the final search.

e1 xe2 xe3 xe4 xe5 x xe6 x xe7 x xe8 x xe9 x x x

e10 x x

full systemA

B

e1 xe2 xe3 xe4 xe5 x xe6 x xe7 x xe8 x xe9 x x x

e10 x x

C1e1 xe2 xe3 xe4 xe5 x xe6 x xe7 x xe8 x xe9 x x x

e10 x x

C2

Figure 4.2: Illustration of the MSO generation process

This method should find most MSOs of the system but can not guarantee a com-plete set as this would require that also the exactly determined sets in B have tobe considered for generation of additional C sets.

These MSOs are then sorted according to which structural faults they could detectin preparation for the residual selection. For this particular system the methodhas produced 1.8 (1776385) million MSOs covering 63604 different combinationsof structural fault detectability.

4.3 Selecting the residuals

To select a sufficient subset of MSOs, a greedy search algorithm is applied [Svärdet al., 2013]. The greedy search works such that a residual is added to the solutionset that fulfils as many new fault isolation requirements as possible. To do thiseach of the residual is tested and the best candidate is included in the solution set.This whole process is repeated until no further improvements are to be found.

4.3.1 System modes

As the approach above were selecting residuals purely based on their structuralfault detectability, except when the residuals were tested, it was clear that someresiduals were only valid in certain modes. This is primarily the case for whichcabin fan currently is in operation. To cope with this all the selected residualswere tested and classified.

Residuals that appeared to be invalid in any mode were completely discardedand the search operation was re-run from that position onward. This is also done


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Figure 4.3: Final structural detectability matrix

on-the-fly when a new residual has been selected. In the case where there existsmultiple MSOs with the same set of structural detectability has only the secondone been tested if the first has been marked as invalid.

Once a general set was generated with ideal isolability without regards of thedifferent modes, the specific mode dependant isolation matrices were analysedand the set was expanded to improve the currently worst matrix using the samemethod. This was iterated until no improvements to any of the isolation matricescould be found. The final structural detectability matrix is shown in Figure 4.3.

4.4 Generating the residual code

During the classification process the residuals have been generated and tested.The residual itself is processed into executable code using a function that will gothrough the directed acyclic graph (DAG) and return the required equations tocalculate each state. A limitation of this function is that it cannot handle when anequation set has to be solved to generate the next needed state, requiring manualsupervision of the output before execution.

As many of the residuals calculate the same states using the same equation sets,another function was made to merge residuals by creating new states and equa-tions when there is a conflict in method to calculate an otherwise shared state.This allows the generation of one function containing all residuals.

4.4 Generating the residual code 31

e49e50e51e52e53e54e55e56e57e59e60e61e62e63e64e65e66e67e68e69e71e76e58e70e77e72e78e74e75e73e76e13e14e15e16e28e36e20e37e35e20e20e20e27e20e35e20e35e20e20e20e20e20e20e35e20e35e35e20e35r314e20e7e8e11e12e38e39e40e41e31e37e6e10e5e9e46e8e12e46e7e11e46e46e31e7e11e46e8e12e6e10e31e5e7e9e11e8e12e28e5e7e8e9e11e12e27e5e6e7e9e10e11e6e10e5e9e31e6e10e5e9e7e11e6e10e37e7e11e8e12e46e31e5e6e8e9e10e12e3e4e34e28e1e2e4e43e44e3e45r113e42e34e3e4e2e43e34e1e3e4e44e37e1e3e4e36e1e2e3e44e2e44e1e45e34e2e1e42e3e2e28e34e3e4e44e9e10e11e7e5e6e34e1e2e4e46e22e21e21e26e21r110e26e21e22r170e25e31e31e22e26e21e26e21e22e22r291e1e2e3e21e22e23e45e20r87e9e10e5e6e22e22e23e22e34e34e23e22e22e23e23e22r326e24e30e31e1e2r102r145e30e24e23e30e24r223r233e30e24e30e24e23e29e33e34e22e33e29e24e30e33e29e33e29e33e29e24e30e32e23e32e29e33e32e32e32e29e33r70e24e30r163e32r214r251r268e32e29e33r192r310e32r75e26e22e21e25e26e23e21e30e24e23e33e29e24e30e32e29e33r132e32r285

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Figure 4.4: Dulmage-Mendelsohn decomposition of the merged residuals

Even though the merging function is far from ideal and can still produce multipleidentical steps the produced output is remarkably faster than running the residu-als separately. The Dulmage-Mendelsohn decomposition of the merged residualsis displayed in Figure 4.4 with horizontal lines marking the splits of the DAG, i.e.,each block of equation can only be calculated by using the states from previousblocks.


4.5 Validation of the residuals

During the classification process a heavily subsampled selection of both trainingdata and the whole data set was used as described in Section3.1. A fault profile,that was repeatedly alternating and increasing in magnitude was injected. Thiswas very effective for the purpose of detecting what modes the residual requiredbut the results could be very hard to interpret.

Due to that a more simple data set have been selected for validation and a singlepercentual fault has been injected. The fault appears at sample 100 as a step of50% of original signal and stays constant for a few samples to then decrease tozero. At sample 300 is the same procedure done in reverse. For easy reference,the fault profile is added to all the figures.

0 100 200 300 400 500

0

0.5

1

Residual 9 (34209) [cfa1]

Fanjclogg Returngridclogg cfa1iF cfa1khF no fault

0 100 200 300 400 500

-0.5

0

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cfa1nF cfa2iF chxfa1F hs2airhumF no fault

Figure 4.5: Validation of residual 9

0 100 200 300 400 500

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Residual 19 (50248) [cfa1 & cfa2]

cfa1iF cfa1khF cfa1nF no fault

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Residual 19 (50248) [cfa1 & cfa2]

cfa2iF cfa2khF cfa2nF chxfa1F no fault

Figure 4.6: Validation of residual 19

4.5 Validation of the residuals 33

The data sets n20090803 and n20091127 have been used respectively for theresiduals that require either CFA1 or CFA2 enabled. Regarding residuals work-ing with both or neither fans have part of both data sets been used, switching atsample 250.

The results are varying quite much among the residuals, some are noisy and onlydetects parts of their indicated faults, like residual 9 displayed in Figure 4.5,while others are clean and produce easily detectable injected faults.

A few of them are not centred around the zero axis, like residual 7 and 8 displayedin Figure 4.7, while are still reacting to the injected fault in a detectable way.

Note that residual 7 produces not-a-number (NaN) with an injection on the TPS2air pressure sensor and could be considered triggering. In the case of residual 19however, should a NaN not be considered an alarm which seems to be requiringa separate handling depending on which of the fans are currently running. Thiscan be seen in Figure 4.6.

Generally, most of the residuals are prone to the fault that they structurally areable to detect, but they all need unique methods of detection, and in some cases,a unique method per fault is required.

0 100 200 300 400 500

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20

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precfaloss returnloss temp3F tps2airpressF no fault

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afs1F afs2F cfa1khF cfa2iF no fault

Figure 4.7: Validation of residual 7 and 8

5Results

In this chapter the results of the design are presented both when detecting oninjected faults and when analysing the real data.

5.1 Fault detection and isolation with injected faults

Analysis of the structural fault detectability and isolability of the final systemyield that 20 of 32 faults are fully isolable. The structural fault isolation matrixis shown in Figure 5.1a.

A manual detection on the residuals with injected single faults cannot with confi-dence detect any air humidity faults. Neither can it detect the cabin fan 1 currentwhen the system is in a cabin fan 2 only mode, which would be expected, butthis brings the question on why this is detected for the opposite case. The restof the faults are all detectable and about half of the faults are possible to isolateby manual analysis of the residuals. The isolation matrix when doing a manualanalysis on injected single faults is presented in Figure 5.1b and show a fair re-duction of isolability which indicate that the residuals selected might not be idealand further iterations of the residual picking process may be needed.

Simple test quantities were produced for automated detection on the real data asmanual detection on the residuals is a time consuming task. The test quantitiesare using either absolute value, cumulative sum (CUSUM) or a windowed meanvalue depending on best detectability on the injected fault data. The thresholdswere then determined automatically using the test validation data so no falsealarms were triggering. For better isolation were the set completed with a few not-a-number test quantities. In Figure 5.1c is the final isolation matrix presented.This indicate that the issue with detecting the air humidity faults were not a

35

36 5 Results

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5.1:Isolationm

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5.2 Detection and isolation on real data 37

residual problem. This automated design can not detect the cabin fan 2 currentfault and a few other pairs can not be isolated from each other.

5.2 Detection and isolation on real data

Analysing the test quantities described in Section 5.1 on the real data yield thatmany of the test quantities triggers and many of the faults are detected but it isnot often a single fault could be isolated with certainty at any given time. Almostall faults isolated are indicated in short time intervals which makes it hard todetermine if these are proper or false alarms. There are however a few cases ofprolonged repeated patterns presented below. Noted can be that less than 3% ofthe time no test quantities are triggering giving us a total of only about 11% ofno fault data including the cases where the data is not properly defined.

The figures throughout this chapters displays the alarms as how many test quan-tities are triggering for this fault in single fault isolation certainty. That meansthat if an alarm is lower than 1 then there is at least one conflict in isolation. Thisis calculated by count of alarming candidates of detected candidates for the spe-cific test quantities triggering. No faults are alarming if only one test quantityis triggering nor if there are contradiction among the test quantities triggering.This is a simple form of isolation by column matching. One shortcoming withthis is that in case of multiple faults could one fault prevent another faults alarmto go off which is also the case for false triggering of test quantities. All faults notcovered in the figures are not alarming during the displayed data set, not evenbriefly.

06:30 06:34 06:38 06:52 18:11 18:26

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irfa

khF

0

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06:30 06:34 06:38 06:52 18:11 18:26

irfa

nF

0

1

Figure 5.2: First noted alarm in the outset data set. The vertical lines indicatetime lapses with missing data in the data set.

Return fan faults

Figure 5.3a displays the alarm signal for fault on the pressure and rotation speedon the return fan on real data from 11th Mars 2008. It cannot isolate the twofaults from each other but it is fairly certain there is a fault on either of those

38 5 Results

two sensors. Also to be considered is that this is not a constant indication whichcould imply that these all are false alarms. There are occasionally some otherfaults alarming, but those are generally brief in nature. Figure 5.2 displays thefirst occurrence of this pattern.

Clogging faults

Another pattern that is repeating over quite large portions of the real data pro-cessed is where there is alarms on multiple parametric faults and pressure sen-sors. These faults are confirmed in Figure 5.1c to be hard to isolate from eachother. A sample of this real data have been selected and is displayed in Fig-ure 5.3b.

19:06 19:21 19:29 19:37 19:46 19:54

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(b) 21th Mars 2008

Figure 5.3: Alarms during selected parts of the outset data set

Outset data set

Both of the above mentioned patterns are part of the a data set, which is thelongest set, containing more than half of the available real data. The name is arbi-trary selected due to this set being the first continuous data available for analysis.This whole set has been marked as return fan loss but there is also documentedunexpected clogging of the return grid. Additionally this data have a fairly lowand inconsistent sampling.

However, the above detected patterns and the documented faults are not entirelycoherent as only half of the data is detected to have return fan problems whilstthe clogging seems to continue further than what has been reported. An overviewof the alarms during this whole set is shown in Figure 5.4 with markings on wherethe part of unexpected clogging is documented.


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Figure 5.4: Full overview of the alarms in the outset data set. The verticallines indicate the start and end of the time period where within also unex-pected clogging of the return grid has been reported.

40 5 Results

HEPA filter clogging

Another data set, covering only the 3rd of August 2009, begins with a fairly clearindication of issues in the post cabin region followed by quite certain alarms onthe HEPA filter. This then stops about 2 am with hardly any alarms until almost 7hours later where yet again issues are indicated in the post cabin fan region andfinally uncertain alarms on quite many of the components in the system. Thealarms over this set can be examined in Figure 5.6.

The data set itself is flagged as unexpected clogging of the return grid, whichcould be one of multiple faults available in this data set, but this is not clearlyisolated and if this design is not producing large quantities of false alarms, is notthe only fault.

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Figure 5.5: Selection of residuals during the 3rd of August 2009.

There are quite a lot of the test quantities triggering in the section where no faultsare being isolated. Figure 5.5 displays a selection of the residuals during a portionof this time frame and clearly displays that something is detected and that thisis a matter of evaluation rather than detection. Included in the graphs is also theno fault data used in the validation for easier comparation. This could indicatea passage with multiple faults. One possible explanation for this could be some-thing faulty with the supply air, like for example smoke, that then propagatesand cloggs most of the system. This has however not been confirmed.


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Figure 5.6: Full overview of the alarms during the 3rd of August 2009.

42 5 Results

Another return fan failure

One data set, covering the 18th of January 2010, is documented as another unex-pected clogging of the return grid. However this is not detected by this design. Itdoes however, briefly, detect but not isolate faults of the parametric type beforeit quite certainly detects faults with the return fan.

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Figure 5.7: Selection of residuals during the 18th of January 2010.

When analysing the test quantities during the silent section of the alarms whichare displayed in Figure 5.8 it is observed that many test quantities trigger about8:23. A selection of the residuals highlighting this event is displayed in Figure 5.7.This indicate that there is something happening that the current evaluation can-not isolate which means that the alarming faults that follow could be a result ofthe measurements taken towards this fault.

Unknown data set

One of the data sets has no documentation of what type of fault could be present,if any. The analysis indicate return fan issues as the alarms displayed in Fig-ure 5.9 tell. However, in general few test quantities trigger which could indicatethat all of these could be false alarms.


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Figure 5.9: Data set with unknown fault.

44 5 Results

No alarms

There was additionally a few sets where the design could not isolate any faults.They are all very similar and only triggers alarms very briefly, mostly on theairflow sensors and post cabin fan loss.

Two of the sets are marked as unexpected return grid clogging, one has docu-mented return fan loss while the last one is supposed to have loss of the supplyfan. Neither of these faults are isolated even though all set are indicated to onlyhave one simultaneous fault.

However, many of the test quantities are either constantly on or triggering onand off repeatedly which could indicate that this section is actually suffering ofmultiple faults which is outside of the scope of the current design.

6Conclusion

In this chapter an overall evaluation of the work will be presented followed byreflections and suggestions for eventual future work.

6.1 Overall evaluation

The expected results were to have an FDI system that detects and isolates faultsin the air loop system that could work in real time and detect the faults withinreasonable time.

On injected data the system is able to detect and isolate most of the modelledfaults. However the models of leaks and cloggings are all parametric faults that,in practice, could be either leakage, clogging or wear.

The system can detect some of the indicated faults in the real data and eventhough it cannot isolate the faults perfectly, it can indicate the nature of thefault which, in practice, is sufficient to know which physical components thatrequires service. The proposed solution shows promising results but additionalwork could improve fault detection and isolation performance by, for example,consider multiple simultaneous faults during the evaluation process.

The system is based on a model of the air loop and is with fairly high certaintyable to detect which mode the fans are operating in even though that informationcurrently not is propagated to the fault detection and isolation level.

The methodology presented in [Svärd, 2012] was partially used and is consideredto be working for this system as a whole. Further development following thismethodology is believed to improve the current results when applicable.

45

46 6 Conclusion

The results on real data indicate that the methods used, even though they areyielding results, are not really sufficient for the design of an FDI solution for thisparticular system. This implies that there is a great potential for further improve-ment if more advanced methods for fault detection and isolation are explored.

6.2 Future work

This section will present suggested future work divided by task in a top-downfashion as it is suggested that future improvements of this design should be madein such an order. This as the underlying model itself is to be considered of desiredquality and without need for immediate improvements.

Fault detection and isolation

The algorithms for detection and isolation should be extended so that multiplesimultaneous faults are handled as there are indications that such exist in some,if not most, sets of real data. It is also recommended that more advanced testquantities are to be generated from the current selection of residuals.

One possible method to improve this could be fault detection and isolation byanalysing the Kullback-Leibler divergence as was originally suggested for thiswork, and should be considered as one of the earlier paths for design improve-ment [Eriksson et al., 2011].

It could also be of interest to make the set of test quantities more dynamic as thismodel has different operation modes. The current solution is fairly basic and themethod described in [Eriksson et al., 2012] could be considered for integration.

Residual generation and selection

For a model with a fair amount of equations, but more importantly, plenty ofinterconnections between them, the MSO count is quickly increasing to sizes thatmakes the residual generation and selection a very time consuming task. Whilethis particular model is yet within reasonable size for modern computers, only afew more additions and it might not be.

Due to this are alternative, preferable automatic, methods of determining whichMSOs to consider for further analysis. One way might be by analysing the residu-als distinguishability [Eriksson et al., 2013], however, as the task to find the MSOsis close to the upper limits of feasibility this analysis must be brought closer tothe model equations. My suggestion would be to analyse the signal (and fault)to noise ratio of the equations themselves and create weights that are to be usedin the MSO search. This would need to be a directional analysis similar to howcausality is handled in [Svärd and Nyberg, 2010] and is particular interesting inthe quantified or otherwise non-linear equations. Due to this is it believed thatthis model could be an interesting set for this kind of analysis even if a (almost)complete MSO set already has been extracted.

6.2 Future work 47

This should not only assist in quicken up the search for MSOs but also should beable to assist in selecting how to structure the MSOs when designing residuals.

Modelling

In the current model the fan modes are estimated on the fly within the model bya simple test quantity, this is needed for making the fan curve equations valid.However, this test quantity is not propagated to the fault detection and isolationlayer of the system. Stall mode is considered a fault and should be implementedand the detection of it ought to be improved. Multiple faults must be handledbefore this is propagated as stall mode occurs fairly often in the live data.

Additionally, in some sets of live data the model has a tough time deciding whichmode the fans are in which in turn gives a very inconsistent output flow for thefans. Analysis indicate that this might not actually be a fault in the model asit not only fits the data fairly well but also fits that parallel fans easily can endup racing each other. In the real data available the sample rate is too low for adeterministic conclusion. However, due to this might it be interesting to smoothout the calculated air flows when problems to determine fan mode occur.

Other improvements of the model could be modelling of the dynamic propertiesand the parametric faults. There has also been some experimentation on makinga Simulink model of the system with fairly decent results, but such an implemen-tation could rapidly make the system infeasible in real time. However it is notsuggested to focus on the underlying models unless no further improvements canbe found in the actual detection and isolation layer of the FDI system.

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