10th

International Symposium on NDT in Aerospace

1 License: https://creativecommons.org/licenses/by-nd/4.0/

Machine Learning based Data Driven Diagnostics &

Prognostics Framework for Aircraft Predictive

Maintenance Partha ADHIKARI

1, Harsha GURURAJA RAO

1, Dipl.-Ing. Matthias BUDERATH

2

1Airbus India, Bangalore, 560 048, India;

E-mail: partha.p.adhikari@airbus.com, harsha.rao@airbus.com 2Airbus Defence and Space, Willy-Messerschmitt-Strasse 81663 Munich,Germany

E-mail: matthias.buderath@airbus.com

Abstract. Recent trends of Digitalization across different industries have led to

generation of massive amounts of data. As a natural consequence, there is a surge in

Advanced Machine Learning techniques being applied to this Big Data.

Simultaneously, there has been growing needs of increased Operational Reliability,

driving down Maintenance costs and increase in Safety, due to which Predictive

Maintenance is quickly becoming the most important strategy across many

industries, especially in Aerospace. As newer Aircrafts are being equipped with

more sensors, Machine Learning based Diagnostics and Prognostics techniques are

becoming increasingly popular compared to conventional approaches in developing

Predictive Maintenance solutions. Building a ML based Diagnostics & Prognostics

Model requires massive amount of Run-to-Fail Sensor data, but the opportunity to

capture this in-service failure related data is very limited in highly reliable & safety

critical aircraft platforms in comparison to other domains. To address this challenge

of lack of adequate & appropriate in-service failure data, Airbus DS has developed a

Simulation Framework in the roadmap of Technology development of ISHM and

Predictive Maintenance. To accelerate the development of Predictive Maintenance

solutions for various aircraft systems, a Data Driven Diagnostics & Prognostics

Framework has been developed. This paper outlines this unique framework and its

validation using the data generated from the ISHM Simulation Framework.

Keywords: Machine Learning, Data Analytics, Diagnostics, Prognostics, Predictive Maintenance,

Simulation, Aircraft Systems, ISHM

1 Introduction

Globally, aircraft operators as well as OEMs are facing major challenges in enhancing,

even meeting specified Mission Success Rate (MSR) and Operational Support Cost targets

due to unscheduled Maintenance leading to Operational Interruptions (OI). Lack of

advanced warning of failure events, fault isolation / troubleshooting overhead and lack of

integrated and optimized planning of fleet, maintenance and logistics are the key

contributors leading to OI. So, Predictive maintenance / ISHM and related technologies are

generally seen as potential strategies to address these problems.

Predictive maintenance provides an integrated solution of assessing present & future

health (diagnostics and prognostics) of complete system and derives planning of operation,

maintenance and logistics. With the rapid growth of technologies such as Internet of Things

(IoT), Machine Learning (ML), Artificial Intelligence (AI) and Big Data technology which

are inherent in Industry 4.0, Predictive maintenance is becoming smarter day-by-day. Thus,

Data-driven and hybrid diagnostics and prognostics solutions are becoming more popular

2

compared to purely model based diagnostics and prognostics. In order to keep up with the

Digital transformation across the enterprise and to speed up the development of integrated

Predictive maintenance solutions across various aircraft systems, a Machine Learning based

Diagnostics and Prognostics (DnP) framework has been developed.

Given the highly reliable and safety critical nature of aircraft systems, it is very

challenging to find sufficient amount of run to fail in-service data. Run-to-fail data is

essential to train purely data-driven predictive model for calculating health grade and

estimating Remaining Useful Life (RUL). Maturity and validation of ML based

Diagnostics and Prognostics framework is supported by simulation strategy realized

through digital twin of integrated predictive maintenance in Airbus DS.

Pundarikaksha Baruah, et. al, 2006 [1], has presented an innovative on-line

approach for autonomous diagnostics and prognostics by developing a “generic”

framework that is relatively independent of the type of physical equipment under

consideration. The framework is based on unsupervised machine learning methods. Honglei

Li, et. al, 2014 [2] presents a diagnostics and prognostics framework tested on data from a

high fidelity equivalent circuit system simulation and empirical component degradation

models. It adapts proven prognostics and health management techniques from machinery to

electronic health management. Hamed Khorasgani, et. al, 2018 [3], claims that both data-

driven and model-based diagnosis methods follow similar procedure and can by brought

under one unifying framework. Their proposed framework for fault detection and isolation

can integrate methods from both data-driven and model based techniques. Zhe Li, 2018 [4],

suggests deep learning based framework for predictive maintenance.

The ML based Diagnostics and Prognostics (DnP) framework discussed within the

scope of this paper (publication) is being developed keeping in mind several different

applications and requirements of various stakeholders. One of them being an array of

options available for conducting advanced ML based techniques for various benchmarking

studies. Since the early stages of development, the framework has been consciously

designed to be generic in nature, to minimize the efforts needed to customize it to different

systems. The DnP framework is based on Python, a popular programming language for

Data Science applications, providing easy deployment options to the Big Data ecosystem.

The subsequent sections describe the proposed ML based Diagnostics and

Prognostics (DnP) framework in detail. Section 2, describes the simulation strategy for

diagnostics and prognostics development. It is followed by section 3, which delves deeper

in to the enablers for diagnostics and prognostics. Section 4 presents the proposed

framework and subsections detail various algorithms incorporated in the diagnostics and

prognostics framework. The framework is applied on NASA engine data and on the

simulation of Electrical and Landing Gear systems within Airbus DS as use-cases and

results are described in section 5.

2 Simulation Strategy for ML based Diagnostics & Prognostics Development

Airbus DS has developed a comprehensive integrated ISHM simulation framework which

contributes in integrated demonstration of Proof of Enablers (PoE), training & maturity of

diagnostics & prognostics algorithm, maturation of ISHM requirements (KPIs) and V&V

of predictive maintenance (ISHM) functions. For end-to-end demonstration of ISHM,

simulation framework hosts simulation of aircraft system with fault injection provision, on-

board health assessment function, off-board analytics related to diagnostics & prognostics,

fleet planning, maintenance/logistics planning, etc. in enterprise level. ISHM instead of

IVHM is used because Health Monitoring and Management is considered as System of

Systems approach.

3

There are three broad categories of condition based diagnostics & prognostics

approaches: Data based, Model based and Hybrid. To develop data driven solution, ML

based Diagnostics & Prognostics (DnP) Framework has been developed. DnP framework

receives data from Big Data environment which is ingested with sensor and BITE data

generated by ISHM simulation framework under different fault scenarios. DnP framework

contains various steps of diagnostics & prognostics algorithms with having provision of

selecting different options for benchmarking study. Initially, study in DnP framework

provides feedback on model fidelity and data generation to ISHM simulation framework.

Key objective of DnP framework is analysing & benchmarking various types of diagnostics

& prognostics algorithms and providing selected trained algorithms for integration and

demonstration of data driven diagnostics & prognostics algorithm in ISHM simulation

framework and in-service deployment (Fig 1).

Fig. 1. ML Based DnP Framework as part of ISHM Simulation Framework

3 Enablers for Diagnostics & Prognostics

Diagnostics & Prognostics are key technology building blocks for predictive maintenance.

Fig. 2 depicts different types of diagnostics & prognostics techniques. There exist the

following three categories of diagnostics and prognostics.

Type I: Reliability Data-based which characterizes the expected lifetime of

the average component operating in historically average conditions (e.g. Weibull Analysis).

Type II: Stress-based which estimates the lifetime of the average component in

a specific environment (e.g. Proportional Hazards Model). Type III: Condition-based which

estimates the lifetime of the specific component in its specific operating environment.

Condition-based maintenance typically falls into three major categories viz. data-

driven, model-based and hybrid. Data-driven Techniques often address only anticipated

fault conditions, where a fault ‘‘model’’ is a construct or a collection of constructs such as

neural networks, expert systems, etc. that must be trained first with known prototype fault

patterns (data) and then employed online to detect and determine the faulty component’s

identity. Model-based Techniques, on the other hand, rely on an accurate dynamic model of

the system and are capable of detecting even unanticipated faults. They take advantage of

the actual system and model outputs to generate a ‘‘discrepancy’’ or residual between the

two outputs that is indicative of a potential fault condition. Hybrid Techniques use

knowledge about the physical process and information from observed data together to

derive advantage of both model based and data driven methods.

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Detailed enablers for data-driven diagnostics are shown in Fig. 2 and sections to

follow, which constitute the proposed ML based diagnostics and prognostics framework.

Fig. 2.Broad categories of Diagnostics & Prognostic techniques and different steps in Data Driven

Diagnostics & Prognostics

4 ML Based Diagnostics and Prognostics (DnP) Framework

DnP Framework has mainly three functions: data pre-processing & feature engineering,

diagnostics and prognostics, each with the provision of selecting techniques from a variety

of different options. Following sub-sections summarize the landscape of various enablers

for different steps of diagnostics & prognostics.

4.1 Data Pre-processing & Feature Engineering

The accuracy in ML based diagnostics and prognostics are primarily driven by the

appropriate data pre-processing, data exploration and feature engineering which are to be

systematically followed.

Data exploration starts with identification of predictor and target variables. In the

context of predictive maintenance, predictor represents features whereas target represents

the health grade or failure modes. Subsequent steps in data exploration are univariate

analysis, bi-variate analysis, missing value treatment, outlier treatment and regime

identification. Flight regime identification in data exploration stage for predictive

maintenance may be necessary if failure propagation happens differently based on different

flight regime. Flight regime are calculated by clustering operating conditions like take-off,

cruise, landing, etc. Statistics of sensor data under different flight regimes are to be

calculated and sensor data segregated in terms of flight regimes.

Next comes feature engineering (Fig. 3) which is a science (or art) of extracting

more information from existing data. Original sensor data are primary features but in many

situations generation of new derived features (FFT, Kurtosis, mean, etc.) may enhance

failure observability of the model. Subsequent steps are feature benchmarking or dimension

reduction.

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Fig. 3. Feature engineering overview

4.2 Anomaly Detection

In Diagnostics phase, the first important step is to detect the presence of an incipient failure

or point of transition from normal state to degraded state within a sub-system component.

Conventionally, this phase is referred as Anomaly Detection (AD). Along with Predictive

Maintenance, there are various use cases for anomaly detection in areas of fraud

prevention, fault detection, and monitoring across many industries such as finance, IT,

security, medical, energy, e-commerce, and social media. Naturally, significant amount of

research is ongoing, to develop advanced methods of Anomaly Detection.

4.2.1 Survey of state-or-art Anomaly Detection Techniques

Anomaly detection started out using statistical methods to detect outliers with “simple

threshold” which was then improved to “dynamic thresholds” based on standard deviations,

moving averages, comparison to a least squares model of the data, statistical comparisons

of very recent data to older data, and histogram analysis. Lately the statistical approaches

have been expanded by Machine Learning methods. Based on the way of training, machine

learning based anomaly detection can be broadly classified in three categories: supervised,

semi-supervised and unsupervised.

Supervised Anomaly Detection describes the setup where the data comprises of

fully labelled training and test datasets. An ordinary classifier can be trained first and

applied afterwards. Semi-supervised Anomaly Detection also uses training and test

datasets, whereas training data only consists of normal data without any anomalies. The

basic idea is that a model of the normal class is learned and anomalies can be detected

afterwards by deviating from that model. Unsupervised Anomaly Detection is the most

flexible setup which does not require any labels. Furthermore, there is also no distinction

between training and test dataset

Unsupervised anomaly detection algorithms can be roughly categorized into the

following main groups (Chandola V et al. 2009 [5]): Nearest-neighbour based techniques,

Clustering based, Statistical algorithms, Subspace techniques, Unsupervised Neural

Network, real time and time series analysis based algorithms. Fig. 4 summarizes the

different categories of anomaly detection.

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Fig. 4. Enablers for state of art anomaly detection

4.2.2 Anomaly Detection in the context of Predictive Maintenance

There are three types of anomalies: point anomalies, sequential anomalies and contextual

anomalies. If a single point deviates from the considered normal pattern it is referred to as a

point anomaly. If a sequence or collection of points is anomalous with respect to the rest of

the data, but not the points themselves, it is referred to as a sequential or collective

anomaly. If a point or a sequence of points is considered as an anomaly with respect to its

local neighbourhood, but not otherwise, it is referred to as a contextual anomaly. In

Predictive maintenance, sequential and contextual anomaly is applicable as it deals with

sensor data under healthy stage of any asset to gradually degraded stages under different

flight regimes.

Almost all types of algorithms as mentioned are applicable for predictive

maintenance. Ramasso1 E., & Saxena A (2014) [6] surveyed extensively on different

technique of anomaly detection related to predictive maintenance. Survey is summarized in

the following table.

Table 1. Survey papers for Anomaly Detection

Category of Anomaly

Detection References

Supervised

Ramasso E. 2009 [7], Ramasso E. et. al. 2010 [8], Ramasso E. et. al.

2013 [9], Sarkar S. et.al 2011 [10], Xue Y. et al. 2011 [11], Zhao D. P.

et al 2011 [12], Yu J 2013 [13], Lin Y. et al. 2013 [14], Tamilselvan P.

et al. 2013 [15]

Semi-Supervised Ramasso E. et al. 2013 [16], Ramasso E. et al 2013 [17], Alestra S. et al

[18]

Unsupervised Wang T. 2010 [19], Wang T. et al 2008 [20], Sarkar S. et al 2011 [10],

Peng Y. et al 2012 [21], Serir L et al 2012 [22], Javed K. et al 2013

[23]

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4.2.3 Time-series / Real-time Anomaly detection

Appropriate pre-processing, feature extraction and suitable customization of algorithms are

needed to address time series data for predictive maintenance. Holt-Winters, Auto-

Regressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR)

are examples of statistical models that take into account seasonality. These models can be

used as a time series dynamic threshold for anomaly detection. Alestra et al. used one class

SVM for detection of degradation. For the modeling of multidimensional degradation

behaviour i.e. degradation trending in time Autoregressive Integrated Moving Average

(ARIMA) model is employed.

In recent years, for anomaly detection of time series data, deep learning Akash Sing,

2017 [24] has emerged as one of the most popular machine learning techniques, yielding

state-of-the-art results for a range of supervised and unsupervised tasks due to its ability to

learn high-level representations relevant for the task at hand. These representations are

learned automatically from data with little or no need of manual feature engineering and

domain expertise. For sequential and temporal data, LSTMs, RNNs have become the deep

learning models of choice because of their ability to learn long-range patterns.

The ability to perform predictive analytics in real-time on streaming data is a game

changer. Works Numenta, 2015 [25], Aggarwal C. et al, 2012 [26], Aggarwal C. C., 2005

[27], Aggarwal C. C. et al, 2011 [28] address application of real time anomaly detection.

The most exciting technique is developed by Numenta is Hierarchical Temporal Memory

(HTM) [25] which is a biologically inspired machine intelligence technology that mimics

the architecture and processes of the neocortex. HTM is a promising candidate for real-time

predictive maintenance.

4.3 Fault Identification & Prognostics

After an anomaly is detected, estimation of the degree of degradation is the next natural

step. This is called Fault Identification which is followed by Prognostics. The main

objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of

a degrading component or system. Anomaly detection and fault isolation are generally

classification or clustering problem whereas Fault Identification & Prognostics are

regression problems. Various techniques in Fault Identification and Prognostics are

summarized in Fig. 5. Broadly there are three categories of prognostics techniques:

similarity based, extrapolation based, and, model identification & estimation based. Table 2

lists some references for each broad category.

Fig. 5. Enablers for Fault Identification & Prognostics

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Table 2. List of references for different prognostics methods

Category of Fault

Identification &

Prognostics

References Remarks

Similarity based

Wang T. et al. 2008 [20], Wang

T. 2010 [19], Peng et al. 2012

[21], Ramasso et al. [16],

Ramasso E. (2014) [29]

[20] [19] →HI-based 3 similarity measures

and kernel smoothing

[21] → Similar to 36 and 37 using 1

similarity measure

[16] → Feature-based similarity, 1

similarity measure, ensemble, degradation

levels classification

[29] → HI-based similarity, polygon

coverage similarity, ensemble

Extrapolation based

– Statistical Regression

Hu C. et al. 2012 [30], Coble J.

2010 [31], Coble & Hines, 2011

[32], Siegel 2009 [33], T. Wang

2010 [19], P. Wang et al. 2012

[34], Juan F. L et al. 2017 [35],

Hu et al., 2012 [30], Liu K. et al.

2013 [36]

[31] [32] → Quadratic fit, Bayesian

updating,

[33] → Logistic Regression,

[19] → Karnel regression, RVM

[34] →RVM

[35]→ GPR

[30]→ RVM, SVM, RNN, Exponential and

quadratic fit, Bayesian updating

[36] → Exponential fit

Extrapolation based

– Neural Network

Riad A. et al. 2010 [37], Jie Liu et

al. [38], Marco R. et al. 2016 [39]

[37] → Application of Adaptive RNN

[39] → ESN

Extrapolation based

– Deep Learning

Gugulothu N. et al. 2017 [40],

Jason Deutsch et al. 2017 [41]

[41] → Embed-RUL is used here.

Model Identification Alestra S. et al. [18] [18] → One class SVM, ARIMA

State Estimation Bhaskar Saha et al. 2008 [42] [42] → RVM, fitting exponential growth

model, Particle Filter

In similarity based method, historical instances of trajectories of different features

of all possible failures are used to create a library. For a given test instance similarity with

instances in library is evaluated to generate a set of RUL estimates. Based on how the

notion of similarity is defined and quantified there are three types of models [43]: Pairwise

similarity model, Hash similarity model & Residual similarity model. Pairwise similarity

model computes the distance between different time series, where distance is defined as

correlation. Hash-similarity model transforms historical degradation data from each

member of ensemble into fixed-size condensed information such as the mean, total power,

maximum or minimum values, or other quantities. Residual-based estimation fits prior data

to model such as an ARMA model or a model that is linear or exponential in usage time. It

then computes the residuals between predicted model data and test data.

Unlike the similarity-based approach, the extrapolation-based approach employs the

training data set not for the comparison with the testing data set but rather for obtaining

prior distributions of the degradation model parameters. The testing data set is then used to

update these prior distributions. An RUL estimate can be obtained by extrapolating the

updated degradation model to a predefined failure threshold. Degradation models can be

constructed using statistical regression, neural network and deep learning approach.

RUL can be estimated using dynamic model identification techniques like AR,

ARX, ARMAX, ARIMA, etc. which are trained (i.e. model coefficients are estimated)

using time series data [43]. Using time series model, features or parameters are predicted to

compute RUL. There are also recursive estimators fitting models in real-time and process

the data, such as recursive ARX and recursive AR. RUL estimation with state estimators

such as Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), and Particle Filter

(PF) works in a similar way. One performs state estimation on some time-varying data, and

predict future state values to determine the time until some state value exceeds some

specified limit or threshold indicating presence of failure.

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4.4 Enhanced Fault Isolation

ML based component level fault isolation in early stage of degradation is carried out by

supervised classification. In most cases residuals computed from sensor measurement and

estimated measurement using physics based model are considered useful features along

with other features e.g. sensor measurements and derived features from sensors. Health

aggregation from subsystem component to subsystem, system and aircraft level is integral

part of diagnostics. Lopez B. et al 2016 [44] implemented component level fault isolation

and health aggregation using Bayesian network based HLR framework.

5 Use cases to validate D&P Framework

The proposed DnP framework shows a series of systematic steps to implement diagnostics

and prognostics of different aircraft systems. Initially, this framework is used to implement

diagnostics & prognostics of Landing Gear and Electrical systems. This in-turn helps to

validate and mature the DnP framework. Turbofan engine simulated data from NASA [45]

is used as third use case to benchmark the algorithms of the framework due to the

popularity of this dataset which is mentioned in more than 100 publications related to

diverse varieties of Diagnostics & Prognostics solutions. Details of fault and sensor

measurements are mentioned for respective use-cases as shown in the Fig. 6.

Fig. 6. Use-cases for DnP Framework

Various steps for use-case study driven by the DnP framework are shown in fig. 7.

Feature engineering study for all systems are provided in Table 3. For Anomaly detection,

One class SVM is chosen to apply for all use-cases, where model is trained using only

selected features in normal operation. For fault isolation bench-marking study was carried

out and K-NN classifier was selected for Engine whereas SVM was chosen for Electrical

system. Different degradation levels (Healthy, Sub-healthy, Degraded, Fail) were obtained

using K-mean clustering of PCA components of selected features. Time series health index

for all training instances was computed using Euclidean distances from fail centroid. Then

degradation model was generated using RVR. The degradation trajectory of testing data is

compared with that of stored training data and the trajectory with closest match are selected

for RUL calculation [46]. This Similarity based approach is used for Engine based on

benchmarking study. For Landing Gear, ARIMA model was sufficient to predict RUL.

Selected results are provided in Fig. 8 to 11 and Table 3, 4 and 5.

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Fig. 7. Various sequential steps for use-case study driven by DnP framework

Table 3. Feature Engineering results

Landing Gear Electrical Engine

No. of original

features (sensor) 9 6 24

New features 2 (Settling time, damping

ratio)

66 (FFT – 36, Kurtosis – 6, Peak to

Peak – 6, Lyapunov Exponent – 6,

Entropy – 6, Correlation – 6)

24 (moving

average)

Features after

benchmarking

3 (Settling time, damping

ratio, shock absorber height) 12 (Peak to Peak – 2, FFT – 10)

12, 8 PCA

components

Fig. 8. Univariate analysis of Shock Absorber of Landing Gear

Fig. 9. Anomaly Detection of Landing Gear : degradation of shock absorber due to Oil leakage.

Original - (left), Zoomed (right)

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Table 4. Benchmarking for fault classification of Electrical System

Sr. No. Algorithm Name Accuracy Precision Recall Training Time (S)

1 Decision Tree 0.99922 0.99922 0.99922 2.80

2 Naive Bayes 0.90429 0.90429 0.90429 0.41

3 SVM 1 1 1 8.54

4 Extra Tree 1 1 1 1.87

5 Random Forest 1 1 0.99929 7.89

6 ADABoost 0.99872 0.99872 0.99872 11.83

7 Logistic 1 1 1 6.54

8 Linear SVC 1 1 1 54.21

Fig. 10. Cluster features with health levels : Engine - (left)

Propagation of health index with cycle number : Engine - (right)

Fig. 11. Health degraded model with fitted RVR : Engine (left), Matched degraded pattern and RUL (right)

Table 5. Benchmarking of various algorithms for RUL calculation: Engine

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6. Conclusion

Extensive survey has been done to find the state-of-the-art techniques of diagnostics and

prognostics and selected algorithms are implemented in this ML based Diagnostics and

Prognostics framework. Key highlights of this framework include its generic nature,

options for benchmarking and ability to be customized for specific systems. This has been

validated and matured with use-cases of diverse systems. However, this framework will

undergo continuous maturity with experience gained in Predictive maintenance across

different aircraft systems.

7 Acknowledgments

The authors are grateful to Gaurav Kumar, Elijah Toppo, Bibhudatta Patnaik, Manish

Patwari, Pandiyaraj Subramani, Ajith Shenoy and Amey Muley for their support. We are

also grateful to reviewers for many of the improvements to the document.

Nomenclature

ABOD Angle Based Outlier Detection

AC Advisory Circular

AD Anomaly Detection

AI Artificial Intelligence

aLOCI Approximate Local Correlation

Integral

AMC Acceptable Means of Compliance

ARMAX Auto-Regressive Integrated Moving

Average

ARIMA Auto-Regressive Integrated Moving

Average

ARP Aerospace Recommended Practice

ARTMAP Adaptive Resonance Theory

Mapping

AWR Airworthiness Report

BIT Built-in Test

BITE Built-in Test Equipment

BN Bayesian Network

uCBLOF Cluster-Based Local Outlier Factor

CBM Condition based maintenance

CC Certification Coordinator

CMGOS Clustering-based Multivariate

Gaussian Outlier Score

COF Connectivity-Based Outlier Factor

CS Certification Specification

DBSCAN Density-Based Spatial Clustering of

Applications with Noise

DnP Diagnostics and Prognostics

DT Decision Tree

EAI Enterprise Application Integration

EKF Extended Kalman Filter

ELM Extreme Learning Machine

EMD Empirical Mode Decomposition

ESN Echo state network

EWPCA Exponentially Weighted Principal

Component Analysis

FFT Fast Fourier Transform

FHA Functional Hazard Analysis

IVHM Integrated Vehicle Heath Monitoring

K-NN k – Nearest Neighbour

KPI Key Performance Index

LDA Linear Discriminant Analysis

LDBSCAN Local-Density-Based Spatial

Clustering of Applications with Noise

LDCOF Local Density Cluster-based Outlier

Factor

LOCI Local Correlation Integral

LOF Local Outlier Factor

LoOP Local Outlier Probability

LRU Line Replaceable Unit

LSTM Long Short Term Memory

LSTM-ED Long Short Term Memory Encoder

Decoder

MAD Mean Absolute Deviation

MAE Mean Absolue Error

MAPE Mean Absolute Percentage Error

ME Mean Error

ML Machine Learning

MLP Multi-Layer Perceptron

MSE Mean Square Error

NFLO Influenced Outlierness

NLARX Non-linear Auto Regressive

Exogenous

NLDR Nonlinear dimensionality reduction

NN Neural Network

OCSVM One Class SVM

OEM Original Equipment Manufacturer

OI Operational Interruptions

OPCAAD Over-sampling PCA on-line anamoly

detection

OR Operational Reliability

ORA Operational Risk Assessment

OSA Open System Architecture

PCA Principal Component Analysis

PF Particle Filter

PHM Prognostic Health Management

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FMECA Failure Modes, Effects, and

Criticality Analysis

FP/FN False Positive / Negative

GMM Gaussian Mixture Model

GPR Gaussian Process Regression

GRU Gated Recurrent Units

GUI Graphical User Interface

HBOS Histogram-based Outlier Score

HILS Hardware in Loop Simulation

HLR High Level Reasoning

HTM Hierarchical Temporal Memory

HUMS Heath Usage Monitoring System

IA Integrity Assessment

ICA Instruction for Continued

Airworthiness

INFLO Influenced Outlierness

IoT Internet of Things

ISHM Integrated System Health

Monitoring

PoE Proof of Enablers

RCM Reliability Centered Maintenance

RMS Root mean square

RNN Recurrent Neural Network

RNN-ED Recurrent Neural Network Encoder

Decoder

RPCA Real Time PCA

RUL Remaining Useful Life

RVM Relevance Vector Machine

RVR Relevance Vector Regression

SHM Structural Health Monitoring

SOM Self-organizing Map

STFT Short time fourier transform

SVM Support Vector Machine

SVR Support Vector Regression

TRL Technology Readiness Level

UKF Unscented Kalman Filter

V&V Verification and Validation

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Biographies

Partha Adhikari – Aerospace professional with more than 20 years of experience in

Integrated Vehicle Health Management (IVHM) / Predictive Maintenance, Predictive Data

Analytics, Navigation, Simulation & Modelling of Aircraft Systems and Avionics

integration. He has served in different techno-management roles in various Aerospace

organisations. Presently he is the IVHM domain lead in Airbus India, Bangalore and is

working on design and development of Data driven Diagnostics and Prognostics, High

level reasoning and tools related to decision support. He has published over 15 papers in the

field of estimation, signal processing and IVHM in national as well as international

conferences and journals.

Harsha Gururaja Rao – Software Engineer with more than 7 years of experience in

designing & developing enterprise level Software applications and Predictive Maintenance

solutions for Aircraft systems. Harsha has a Bachelor of Engineering Degree in Computer

Science from the Visvesaraya Technological University. Harsha, in his current role as an

Engineer at Airbus India, Bangalore is working on developing Data driven solutions to

meet Aerospace & Defense business systems requirements in the areas of IVHM and

Predictive Maintenance.

Matthias Buderath - Aeronautical Engineer with more than 30 years of experience in

structural design, system engineering and product and service support. His main expertise

and competency is in system integrity management, service solution architecture and

Integrated System Health Monitoring and Management. Today he is head of Airbus

Defence and Space R&T Strategy and Senior Expert Integrated System / Aircraft Health

Monitoring and Management. He is member of international Working Groups covering

Through Life Cycle Management, Integrated System Health Management and Structural

Health Management. He has published more than 80 papers in the field of Structural Health

Management, Integrated Health Monitoring and Management, Structural Integrity

Programme Management and Maintenance and Fleet Information Management Systems.