tools for sensor performance...

Tools for Sensor Tools for Sensor Performance Assessment Performance Assessment

Research Assistants/Staff: Faculty:L. L. JiangJiang D. D. DjurdjanovicDjurdjanovic, , J. NiJ. Ni

For more information, contact Prof. J. Ni; Phone: 734-936-2918; Email: [email protected]

ObjectivesObjectives

StateState--ofof--thethe--ArtArt

ApproachesApproaches

Case StudyCase Study

Future WorkFuture Work

• To detect sensor progressive degradation and predict sensor failures and reconstruct the degrading sensor readings

• Direct hardware redundancy method: use two or more sensors to measure the same key variable and assess sensor performance by comparing the consistency between different sensors, simple to implement but potentially costly

• Functional redundancy method: use multiple sensors to measure related variables and validate sensor readings by employing sensor fusion techniques, more complex but cheaper approach

• Zero-lag correlation based direct hardware redundancy method

Truncated Features

M – Step Window

Sensor 1: x1 (0) x1 (1) x1 (2) …… x1 (M-1) x1 (M) x1 (M+1) ……



Extracted Features from Collected Data

……

Exponential Weighted Moving Average (EWMA) Control Chart

Extract Key Elements: C21 (t) C31 (t) C32 (t)from the Symmetric Correlation Matrix W (t)

Zero-Lag Correlation

11 12 13

21 22 23

31 32 33

(1) (1) (1)(1) (1) (1) (1)

(1) (1) (1)

C C CW C C C

C C C

⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦

W (3)

Moving the WindowOne Step Ahead

W (2) ……

• Time-series modeling based functional redundancy method

General Model

0 01

2 0

( ) ( ) ( ) ( ) ( )( )( )( ) ( ) ( ) ( ) ( )

out out in

in out in

G s G s A s A s B sS sG sS s G s B s B s A s

= = =

Transfer Function

By fitting Auto-Regressive Moving Average Vector (ARMAV) time series model to the data within an extending window, the dynamics of the plant and input/output sensor can be identified by finding the zeros and poles of the transfer function.

Sensor with First Order Dynamics

System Function

H(s) = K / (τs + 1)

Time [s]

With Degradation

Without Degradation

Inte

nsity

[V2 ]

Output Signal with Noise

U (t)

t [ms]T0 T

A0

Input Signal

Product Height Related

Product Width Related

θ

Step Input Response Curve

Nor

mal

ized

Out

puts

Time [ms]

Tangent Line at t = 0 msτ = 1 / tanθ

Period Index

Tim

e C

onst

ant [

ms]

Key Parameter for Sensor Degradation Detection

Increasing Trend

Feature Extraction

Zero-lag Correlation Based Hardware Redundancy Method

With Degradation

Without Degradation

Tim

e C

onst

ant C

orre

latio

n [m

s2 ]

Period Index

UCL MEAN LCL

EWMA Curve

Degradation Detected Period Index = 345

Tim

e C

onst

ant C

orre

latio

n [m

s2 ]

Period Index

• Evaluate both methods with real industrial data instead of simulation data

• Develop algorithm for sensor data reconstruction

Methods

Detection of Intermittent Connections in Detection of Intermittent Connections in An Industrial NetworkAn Industrial Network

Research Assistants/Staff: Faculty:YY. . LeiLei J. Ni, D. J. Ni, D. DjurdjanovicDjurdjanovic




ApproachesApproaches

AccomplishmentsAccomplishments


SponsorsSponsors

• Provide network performance and diagnostic information at the device level and network level.

• Develop novel network health monitoring tools for plant floor network systems.

• Minimize network diagnosing time due to intermittent connections on the network.

• Classification Result of Error Source

• Intermittent Connection is difficult to identify.• No tool available for Intermittent Connection detection

• Network health monitoring framework

• Error Source Detection

DNETInterface

DAQInterface

Feature Extraction &

Error Source Detection

Network

Bus-off Hitting Time&

Error-Passive Hitting Time

Intermittent LocationInference

Network Traffic Stochastic Pattern

&Error Estimation

DNETInterface

DAQInterface

Feature Extraction &

Error Packet Pattern Recognition

Network

Node waveform Feature Database

• Developed Error Source Detection System

Error Packet

Packet

2.65 2.7 2.75 2.8 2.85 2.91.7

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

2.6

Mean Voltage of Common Mode Signal (V)

Mea

n V

olta

ge o

f log

ic 0

(V)

Node 3Node 10Error Packet

• Identification of Error Location • Intermittent connection scenarios

BB AA

B AEB C

Normal Packet

BB AA

A

BE

Normal PacketError Packet

AE

A B

Receiving Mode Transmitting Mode

• Determination of the error location• Assume the intermittent connection locate at the

drop cable of a node• If we observe two patterns happens

coincidentally– Node A send error packet after normal packets– Error packets followed by packet sent by node A

• Then Node A has intermittent connection problem.

• Conduct plant validations

• Construct intermittent connection model and estimate nodes bus-off time

• is the rate of transmission of non-corrupted messages for station k

• is the rate of corrupted messages•

0kλ

1kλ

0 0k k kλ λ λ= + Error

Active

ErrorPassive

BusOff

Special command

TEC > 255

REC <=127

& TEC <=127REC >127 or TEC> 127 & TEC <=255

ErrorActive

ErrorPassive

BusOff

Special command

TEC > 255

REC <=127

& TEC <=127REC >127 or TEC> 127 & TEC <=255

Init

• Network Node Health Modeling

• NSF I/UCRC for Intelligent Maintenance Systems



ApproachApproach

AccomplishmentsAccomplishments


SponsorsSponsors

• Use only the load data (amount of current) from the spindle and 5 servo motor drives to track mill bit health

• Explore correlation between servo motor current and cutting force in an oblique cutting

• Employ Time Frequency Analysis and other recently-developed methods to extract features from load data.

Effect of filtering on data.


• Filter raw load data using low-pass filter

6500 6550 6600 6650 6700 67500

1

2

3

4

5

6

7

8

9

10

6500 6550 6600 6650 6700 6750

0

1

2

3

4

5

6

7

8

9

10

Load

(V)

Time (s)

Filte

red

Load

(V)

Time (s)

• Partition filtered data to separate milling passes based on varying levels in servo-motor current

• Extract features from filtered data using TFA

Spindle Motor Current Spindle Motor Current

Time Frequency

Distribution6500 6550 6600 6650 6700 6750

0

1

2

3

4

5

6

7

8

9

10

Filte

red

Load

(V)

Time (s)

Spindle Motor Current

Time (s)

Freq

uenc

y (H

z)• Track 5-axis mill health under dynamically-varying,

oblique loading conditions

• Improve tracking and prediction of process health for various machines through signal processing

• Propose a general method for tracking the health of a mill bit without adding sensors to the system

• Decrease the overall cost of milling compressor blades by using a condition-based method to change mill bits

Extraction of features using Time Frequency Analysis

Cutting Process Monitoring in Cutting Process Monitoring in Turbine Blade MachiningTurbine Blade Machining

Research Assistants/Staff: Faculty:Adam Brzezinski Adam Brzezinski D. D. DjurdjanovicDjurdjanovic, J. Ni, J. Ni

• This research is supported by General Electric Aviation. My contact at GE is Roger Lindle.

• Finish characterization of mill health

• Install sensors and begin collecting data on grinder

• Integrate and process data from multiple sensors with various frequency ranges for the grinder project

Typical Confidence Value Plot

Amount of Warning Time from Raw Data

Time (s)

Load

(V)

Service WindowMachine

Shutdown

% H

ealth

of M

ill B

it

% of Program Completion

Decay as mill bit dulls

• Found method for properly partitioning data

• Determined predictive model for health of mill bit

• Produced CV degradation trends for load data

• Provided warning time (based on raw data) before unexpected shutdown happens

X-axis data

Spindle data

Warning of shutdown based on level of X-axis signal

Decay of mill bit health based on drift of process features

Immune System Engineering for Immune System Engineering for Automotive SystemsAutomotive Systems

Research Assistants/Staff: Faculty:ShiminShimin DuanDuan J. NiJ. Ni

D. DjurdjanovicD. Djurdjanovic


ObjectivesObjectives Immune System for the Automotive Immune System for the Automotive Engine SystemEngine System

Proposed Research Task and Time TableProposed Research Task and Time Table

Project Leadership and ManagementProject Leadership and Management

• Develop novel approach to realize “immune systems”functionalities in automated systems.

• Robustly detect abnormal behavior, isolate source, and compensate for negative effects to achieve desired performance in spite of the presence of an anomaly.

An anomaly effects automated system like a virus effects human

• PI: Dr. Dragan Djurdjanovic, University of Michigan • Co-PI: Prof. Jun Ni, University of Michigan

Kenneth Marko, ETAS Inc

Problem StatementProblem Statement• The goal of the proposed research will be to incorporate

the natural immune system functionalities and uniqueness into automotive engine system. There are three primary parts for accomplishing this target:

• Anomaly Detection Agents (ADA-s)-Identify the intrusion depicted in the degraded system-Isolate the intrusion source by reconfiguring, reconnecting and decomposing detection agents.

• Diagnostic Agents (DA-s)-Recognize and describe the anomaly source character if input/ output patterns have been observed in the past.-In case input/output patterns have not been seen in the past, create a new DA for recognizing the new anomaly next time.

• Control Agents (CA-s)-Utilize fault characterization from DAs to postulate control laws for restoring the performance of the anomalous subsystem as much as possible.

Proposed immune system operation for an automotive system

Multiplication of ADA-s, DA-s and CA-s in the electronic throttle mechanism

Subtask 3.1: Design of CA-s for recovering performance

Task 3: Methods for Control System Reconfiguration

Subtask 3.2: Implementation on an engine subsystem

Subtask 2.3: Demonstration on an engine subsystem

Subtask 2.2: Development of identification methods

Subtask 2.1: Methods for realization of DA-s

Task 2: Methods for Diagnosis of Anomalous Condition

Subtask 1.3: Practical implementation of ADA-s

Subtask 1.2: Isolation of the Anomaly Source

Subtask 1.1: Multi-regime anomaly detection

Task 1: Methods for Anomaly Detection and Isolation

31-3625-3019-2413-187-120-6Tasks

Month

The research activity for over a three-year period

tools for sensor performance...

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