From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics

Table of Contents

Cover

Title

Copyright

Introduction

I.1. From the reinforcement of techno-socio-economic issues…

I.2. To the apparition of a topic: PHM…

I.3. To the purpose of this book…

1 PHM and Predictive Maintenance

1.1. Anticipative maintenance and prognostics

1.2. Prognostics and estimation of the remaining useful life (RUL)

1.3. From data to decisions: the PHM process

1.4. Scope of the book

2 Acquisition: From System to Data

2.1. Motivation and content

2.2. Critical components and physical parameters

2.3. Data acquisition and storage

2.4. Case study: toward the PHM of bearings

2.5. Partial synthesis

3 Processing: From Data to Health Indicators

3.1. Motivation and content

3.2. Feature extraction

3.3. Feature reduction/selection

3.4. Construction of health indicators

3.5. Partial synthesis

4 Health Assessment, Prognostics and Remaining Useful Life - Part A

4.1. Motivation and content

4.2. Features prediction by means of connectionist networks

4.3. Classification of states and RUL estimation

4.4. Application and discussion

4.5. Partial synthesis

5 Health Assessment, Prognostics, and Remaining Useful Life - Part B

5.1. Motivation and object

5.2. Modeling and estimation of the health state

5.3. Behavior prediction and RUL estimation

5.4. Application and discussion

5.5. Partial synthesis

Conclusion and Open Issues

C.1. Summary

C.2. The international PHM: open questions

Bibliography

Index

End User License Agreement

List of Tables

2 Acquisition: From System to Data

Table 2.1. Main methods used for risks evaluation and management

Table 2.2. Examples of physical quantities to observe [CHE 08a]

Table 2.3. Failure distribution of asynchronous motors

Table 2.4. Examples of experiments performed on Pronostia platform

3 Processing: From Data to Health Indicators

Table 3.1. Examples of temporal features

Table 3.2. C-MAPSS features

Table 3.3. TURBOFAN–parameterization of predictive tools

Table 3.4. TURBOFAN - predictability of features from F1 to F8

Table 3.5. TURBOFAN - RUL estimation error with ANFIS

4 Health Assessment, Prognostics and Remaining Useful Life - Part A

Table 4.1. TURBOFAN – Synthesis of prediction performances

Table 4.2. Datasets for SW-ELM performance tests

Table 4.3. Comparison between the performances of the models

Table 4.4. Characteristics of cutting tools

Table 4.5. Performances of robustness and applicability for a cutting tool

Table 4.6. Performances of reliability and applicability for three cutting tools

Table 4.7. Performances of reliability and applicability for unknown data

Table 4.8. Performances of reliability and applicability with an ensemble of SW-ELM

Table 4.9. PHM issues and S-MEFC algorithm

Table 4.10. TURBOFAN - prognostics by dynamic thresholding - results of tests

5 Health Assessment, Prognostics, and Remaining Useful Life - Part B

Table 5.1. Classification of Markov processes [SOL 06a]

Table 5.2. Algorithms for learning the parameters of a DBN [BEN 03a, MUL 05]

Table 5.3. Inference methods for a DBN (see [MUR 02] for further details)

Table 5.4. Parameters estimated within 10-3 mm

Conclusion and Open Issues

Table C.1. Towards a new generation of PHM modules?

List of Illustrations

Introduction

Figure I.1: Publications with PHM as a topic (Web of Sciences, February 2016)

1 PHM and Predictive Maintenance

Figure 1.1 Forms of maintenance according to the standard EN 13306 (2001) [EN 01]. For the color version of this Figure, see www.iste.co.uk/zerhouni1/phm.zip

Figure 1.2. Illustration of prognostic process. For the color version of this Figure, see www.iste.co.uk/zerhouni1/phm.zip

Figure 1.3. Taxonomy of prognostic approaches

Figure 1.4. Hybrid prognostic approaches

Figure 1.5. Complementarity of detection, diagnostic, and prognostic activities [GOU 11]

Figure 1.6. PHM cycle as an adaptation of the OSA-CBM architecture. For the color version of this Figure, see www.iste.co.uk/zerhouni1/phm.zip

2 Acquisition: From System to Data

Figure 2.1. From system to PHM data - steps

Figure 2.2. Choice of critical components - approach

Figure 2.3. Structure of an acquisition process [ASC 03]

Figure 2.4. Examples of sensors of force and quartz accelerometers

Figure 2.5. Simplified and illustrated structure of an acquisition chain

Figure 2.6. Acquisition card for vibrations (a), temperature (b), and external frame for the computer (c)

Figure 2.7. Subsystems of a train

Figure 2.8. Critical components of a train’s motor

Figure 2.9. Experimental bench Pronostia and NSK 6804DD bearings used

Figure 2.10. Bench elements applying a radial force: pneumatic jack and proportional pressure regulator. For the color version of this Figure, see www.iste.co.uk/zerhouni1/phm.zip

Figure 2.11. Gear system and load transmission

Figure 2.12. Examples of degradation of outer and inner races of a bearing

Figure 2.13. Raw vibration signals obtained for two bearings under test

3 Processing: From Data to Health Indicators

Figure 3.1. Feature extraction, selection and reduction, and construction of health indicators

Figure 3.2. Feature extraction techniques, adapted from [YAN 08]

Figure 3.3. RMS, Kurtosis and fast Fourier transform of vibration signals produced on Pronostia platform

Figure 3.4. Short-time Fourier transform of a vibration signal generated on Pronostia platform. For the color version of this Figure, see www.iste.co.uk/zerhouni1/phm.zip

Figure 3.5. Illustration of wavelet packet decomposition of second order

Figure 3.6. WPD (1-800 Hz) of vibration signals produced on Pronostia platform (vertical and horizontal accelerometers)

Figure 3.7. Signal x(t) and its upper and lower envelopes

Figure 3.8. First extracted IMF

Figure 3.9. Decomposition process to obtain IMFs

Figure 3.10. Variation of the EMD residual as a function of the health state of the component

Figure 3.11. Examples of results obtained from vibration signals gathered on Pronostia platform: EMDhealthy EMD of a new bearing, EMDfaulty EMD of a faulty bearing, THHhealthy HHT of a new bearing, and THHfaulty HHT of a faulty bearing

Figure 3.12. Data reduction methods

Figure 3.13. PCA: reduction of data gathered on Pronostia (three to two dimensions)

Figure 3.14. Principle of kernel PCA, adapted from [BIS 06]

Figure 3.15. Illustration of the kernel trick process

Figure 3.16. Kernel PCA: reduction of data gathered on Pronostia

Figure 3.17. Illustration of ISOMAP steps [TEN 00]: (A) geodesic distance between two points, (B) neighborhood graph and approximation of geodesic distance by the shortest path between two points of the graph, (C) projection of data onto a space of dimension 2, where the geodesic distance is approximated by the straight line.

Figure 3.18. ISOMAP: extraction/reduction of two, then of one feature from vibration data gathered on Pronostia

Figure 3.19. Predictability concept

Figure 3.20. Selection of predictable features

Figure 3.21. Turboreactor [FRE 07]

Figure 3.22. TURBOFAN–prediction of feature F5 and associated predictability

Figure 3.23. TURBOFAN - predictability of features for H = 134 time-units

Figure 3.24. TURBOFAN - RUL estimation on a test sequence

Figure 3.25. Construction of health indicators based on the Hilbert-Huang transform

Figure 3.26. Illustration of IMFs selection

Figure 3.27. Health indicators obtained by using Hilbert-Huang transform

4 Health Assessment, Prognostics and Remaining Useful Life - Part A

Figure 4.1. From data to RUL-prediction and classification

Figure 4.2. Representation and taxonomy of long-term prediction approaches [GOU 12]

Figure 4.3. Schematization of long-term prediction approaches

Figure 4.4. Data sequences from NN3 competition (randomly chosen)

Figure 4.5. NN3–RMSE vs. processing time

Figure 4.6. Data sequences from TURBOFAN application (randomly chosen)

Figure 4.7. TURBOFAN - State classification and RUL estimation

Figure 4.8. Wavelet neural network of the SW–ELM [JAV 14b]

Figure 4.9. SW-ELM set and uncertainties of estimates

Figure 4.10. Wear estimation of cutting tools–methodology

Figure 4.11. Robustness and reliability tests

Figure 4.12. Prediction of wear of tools and confidence intervals at 95%

Figure 4.13. Classification of states and RUL estimation

Figure 4.14. States classification–problems inherent to the learning phase [GOU 13]

Figure 4.15. Classification methods and PHM (adapted from [GOU 13])

Figure 4.16. Prognostics without a priori information about the thresholds–overall synoptic

Figure 4.17. Offline phase: learning of predictors and classifiers

Figure 4.18. Online phase: predictions and estimations of states

Figure 4.19. TURBOFAN - distribution of measurements and lifespans. For the color version of this Figure, see www.iste.co.uk/zerhouni1/phm.zip

Figure 4.20. TURBOFAN – prediction error interval

Figure 4.21. TURBOFAN - RUL estimation by automatic thresholding (Test 1)

Figure 4.22. TURBOFAN - illustration of classes of variable states

Figure 4.23. TURBOFAN - dynamic assignment of failure thresholds

Figure 4.24. TURBOFAN - Estimated and actual RUL (for 100 tests)

Figure 4.25. TURBOFAN - pdf of the RUL: a) proposed approach, b) according to [RAM 13b]

5 Health Assessment, Prognostics, and Remaining Useful Life - Part B

Figure 5.1. Learning and exploitation of degradation models for the prognostics

Figure 5.2. Example of discrete Markov chain

Figure 5.3. Hidden Markov Model (HMM)

Figure 5.4. Representation of a stochastic Markov process by means of BN and DBN: ExRB expanded solution (BN) and ExRBD compact solution, according to [MUL 05]

Figure 5.5. Representation of HMM by means of a DBN developed over 3 instants

Figure 5.6. Representation of a HMM by means of a DBN: DBNHMMdet details of nodes and DBNHMMgen general compact version where P[X1 = i] = π(i), P[Xt] = P[Xt = xj|Xt-1 = xi] = A(i, j), and P[Ot = vk|Xt = xi] = B = {bi(k)}

Figure 5.7. Representation of a MoG-HMM by means of a DBN: MoGHMMRBDder model developed over three instants and MoGDBNHMMgen general compact version

Figure 5.8. Sensitivity study for determination of the number of mixtures M

Figure 5.9. Example of state sequence obtained by Viterbi algorithm

Figure 5.10. Selection of the best model that represents the current observations

Figure 5.11. Definition of short and long path

Figure 5.12. Machining test bench [PHM 10]

Figure 5.13. Clustering of states of the cutting tools

Figure 5.14. State sequence for a record of wear evolution

Figure 5.15. State sequence

Figure 5.16. W6 wear estimation for the tool 6, by using the data from tool 1 and 4 as learning data, and RUL6 predicted RUL for the tool 6

Conclusion and Open Issues

Figure C.1. PHM approach and V&V process

Reliability of Multiphysical Systems Set

coordinated by

Abdelkhalak El Hami

Volume 4

From Prognostics and Health Systems Management to Predictive Maintenance 1

Monitoring and Prognostics

Rafael Gouriveau

Kamal Medjaher

Noureddine Zerhouni

First published 2016 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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© ISTE Ltd 2016

The rights of Rafael Gouriveau, Kamal Medjaher and Noureddine Zerhouni to be identified as the author of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2016947860

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-84821-937-3

Introduction

I.1. From the reinforcement of techno-socio-economic issues…

The RAMS services (reliability, availability, maintainability, and safety) today are widely applied to perform limited studies or in-depth analysis. Indeed, industrial maintenance appears to be the source and the target of scientific developments, which is reflected into specific actions of partnership industry research, or projects of greater scope, such as the one of the IMS center1. In a more focused way, at a business level, the traditional concepts of predictive and corrective maintenance are being gradually completed by taking into account the failure mechanisms in a more proactive way [HES 08, MUL 08b]; industrialists tend to strengthen their ability to anticipate failures in order to resort to the most correct possible preventive actions with a goal of reducing costs and risks. Therefore, the implementation of solutions of Prognostics and Health Management (PHM) plays a growing role, and the prognostics process is considered today as one of the main levers in the research of global performance.

– First of all, the failure anticipation of critical elements foresees industrial risks and assures the safety of people and goods.

– Then, prognostics assures a continuity of services, and hence increases their quality.

– Additionally, in environmental terms, industrial prognostics is in line with sustainable development principles: it increases the availability and lengthens the life cycle of industrial systems.

– Finally, implementing predictive maintenance (based on prognostics) requires a qualification and contributes to the development of the technical maintenance staff.

I.2. To the apparition of a topic: PHM…

Beyond the reaction that it can encounter among the industrial world, this topic of prognostics or PHM becomes naturally a research framework in its own right, and tends to be more and more visible within the scientific community. Several laboratories are interested in it today (NASA PCoE, Atlanta University, IMS Center and Army Research Lab in the USA, Toronto University in Canada, CityU-PHM Center Hong-Kong University, etc.), and every year, four conferences dedicated to the PHM topic are held2, two of which are supported by the IEEE Reliability Society. This is an indicator of the growing awareness of this topic and, moreover, that the research studies in this domain are seeing rapid growth (Figure I.1).

Figure I.1. Publications with PHM as a topic (Web of Sciences, February 2016)

I.3. To the purpose of this book…

In addition to the evident rise of this topic, PHM solutions are the result of the evolution of methods and technologies of dependability, monitoring and maintenance engineering. This book fits into this context. Our goal is to present the appearance of this PHM topic, to show how it completes the traditional maintenance activities, to highlight the underlying problems, to describe the advantages that can be expected from implementing PHM solutions in industry and, finally, to consider the major problems and challenges which are still relevant today. For this purpose, the book is structured as follows:

– Chapter 1 - PHM and Predictive Maintenance. The first chapter covers the general presentation of the PHM process. Here