From Prognostics and Health Systems Management to Predictive Maintenance 1 E-Book

Rafael Gouriveau

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2016

Seitenzahl: 243

Beschreibung

This book addresses the steps needed to monitor health assessment systems and the anticipation of their failures: choice and location of sensors, data acquisition and processing, health assessment and prediction of the duration of residual useful life. The digital revolution and mechatronics foreshadowed the advent of the 4.0 industry where equipment has the ability to communicate. The ubiquity of sensors (300,000 sensors in the new generations of aircraft) produces a flood of data requiring us to give meaning to information and leads to the need for efficient processing and a relevant interpretation. The process of traceability and capitalization of data is a key element in the context of the evolution of the maintenance towards predictive strategies.

Leseprobe

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?

Landmarks

Cover

Table of Contents

Begin Reading

Pages

C1

iii

iv

v

ix

x

xi

1

2

3

4

5

6

7

8

9

10

11

12

13

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

G1

G2

G3

G4

G5

Reliability of Multiphysical Systems Set

coordinated byAbdelkhalak 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:

ISTE Ltd

27-37 St George’s Road

London SW19 4EU

UK

www.iste.co.uk

John Wiley & Sons, Inc.

111 River Street

Hoboken, NJ 07030

USA

www.wiley.com

© 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 we highlight the strategic urgency to take the failure mechanisms into account in a more proactive way, and we describe the evolution of related challenges and prerogatives of the maintenance services. In the following, we introduce the PHM activity, and, more specifically, the prognostic process that lies beneath. This chapter defines a coherent set of treatments that are necessary to implement a PHM approach; these different basic building blocks are developed in the following chapters.

–

Chapter 2

- Acquisition: from System to Data

. In order to deploy PHM, one must be able to observe the behavior of the analyzed system. This is what the second chapter deals with; here we suggest an approach for the generation of monitoring data representative of degradation mechanisms in critical components.

–

Chapter 3

- Processing: from Data to Health Indicators

. The acquired raw data from the system under consideration (

Chapter 2

) must generally be preprocessed, in order to extract and select the descriptors that help, over time, to reveal the (mal)function.

Chapter 3

deals with this aspect; here we describe the usual tried and tested approaches for creating health indicators.

–

Chapters 4

and

5

- Heath Assessment and Prognostics of Residual Lifetime

. These two last chapters (excluding the conclusion) focus on the development of models and/or methods for estimating the state of health, and of prognostics of behavior of the monitored system. Thus, we describe how to exploit the information produced in the previous steps in order to estimate the residual lifetime, and to associate to it a confidence measure. We also show how the variability of operating conditions and mission profiles, as well as the physical unawareness of transient states, impact health state modeling and the ensuing performances.

In the conclusion, we deliver a critical view on the maturity of PHM activity, and we open a discussion, on one hand on the problems which remain open to the international context, on the other hand on the decision-making process that stems from these problems. The latter aspect is the subject of the book From PHM Concept to Predictive Maintenance 2 [CHE 16] in which we address the aspects regarding the strategic processes of maintenance decisions, and, more broadly, the life cycle management of product/equipment: the gathered data is traced and transformed into knowledge in order to support the decision-making concerning maintenance, re-design or recycling of equipment.

1

IMS : Industry-University Cooperative Research Center for Intelligence Maintenance System “

The vision […] is to enable […] to achieve and sustain near-zero breakdown performance

”. (

http://www.imscenter.net/

).

2

IEEE International Conference on Prognostics and Health Management, Prognostics and System Health Management Conference, Annual Conference of the Prognostics and Health Management Society and the European Conference of the Prognostics and Health Management Society.

1PHM and Predictive Maintenance

1.1. Anticipative maintenance and prognostics

1.1.1. New challenges and evolution of the maintenance function

1.1.1.1. Industrial maintenance

According to the standard EN 13306 (2001), maintenance can be defined as a “combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it in, or restore it to, a state in which it can perform the required function” [EN 01]. It also includes a set of actions of troubleshooting, repairing, controlling and verifying physical equipment, and it should contribute to the improvement of industrial processes. From the traditional viewpoint, the maintenance function guarantees dependability characteristics of equipment, in particular its availability. Therefore, globally it aims to understand the failure mechanisms and to act accordingly in order to ensure that the system (the good) can perform the function that it has been conceived for. However, the missions of the maintenance function are no longer limited to the implementation of means to ensure the “goods service”. Different requirements in terms of quality, safety and costs have emerged, and the challenges and prerogatives of the maintenance function have evolved in the last 20 years.

1.1.1.2. Challenges and prerogatives of the maintenance function

The challenges of the maintenance function can be discussed from different points of view. First of all, as industrial equipment becomes more and more complex, it requires greater competence in maintenance. Furthermore, the company evolves within a strongly competitive environment and the financial concerns are very prominent. Therefore, maintenance doesn’t escape the cost reduction rule. At another level, for some years, the industrial managers have been facing more significant environmental and social constraints. It is not sufficient anymore to be content with technical and economic performance, but it becomes necessary, or even compulsory, to take into account the environmental “constraints”: a factory produces waste, pollutes, and contributes to the greenhouse effect, etc. This is coupled with the respect for human dignity, which constitutes a social constraint. The latter aspects have recently led to drafting of legislative texts that strongly encourage companies to include the notion of sustainable development in their strategy. The concrete result is the pursuit of a triple performance, where business performance of course remains essential, but is also complemented by new human/social and environmental requirements. The prerogatives of the maintenance function have thus been studied, and it has had to evolve with regard to the growing challenges:

– It aims to increment the equipment availability while reducing the direct exploitation costs (technical and economic).

– It has to ensure a safe operation of equipment, namely avoiding accidents which can be judged as detrimental to the environment (environmental).

– It is responsible for satisfactory work conditions and for human safety (social).

1.1.1.3. Evolution of the maintenance function

In view of the ever-growing requirements, maintenance costs have rapidly risen in recent years. For example, it is estimated that in the USA, the maintenance costs amounted to $200 billion in 1979, and that they have seen a growth of about 10 to 15% in the following years [BEN 04]. However, an important part of this maintenance cost could be avoided: poor planning leads to waste of supplementary maintenance hours, perhaps on equipment without a major role in production continuity. This increase of costs alone doesn’t justify the need to reconsider the traditional maintenance approaches. First of all, production systems evolve continuously and newer technologies have appeared, thanks to automation (as machines could ensure production without human intervention). Next, companies seek to rapidly adapt the production quantity and quality in relation to variations in client’s demand, which requires a high level of flexibility for the industrial equipment. Therefore, although maintenance activity is today considered an activity in its own right, the companies do not hesitate anymore to outsource it in order to benefit from the strong core competencies of the service providers. This evolution is due to a large extent to the development of sciences and technologies of information and communication (STIC). At another level, in the last few years, new maintenance architectures have appeared. One of the most recent is that of s-maintenance (“s” for semantics). This maintenance incorporates the concept of e-maintenance, and it is based on the principle of sharing and generating knowledge, formalized by ontology [KAR 09b]. However, before the development of maintenance architecture aiming to reduce the distance between these actors, it is the maintenance strategies themselves that evolve. Indeed, today the maintainers wish to go beyond the static maintenance (without an anticipation of the evolution of the equipment state), and to implement more “dynamic” maintenance strategies. The following section is dedicated to the analysis of this evolution.

1.1.2. Towards an anticipation of failure mechanisms

1.1.2.1. Cartography of maintenance forms

Before the 1960s, the main mission of a company’s maintenance service was the intervention on broken equipment in order to repair it as soon as possible. This kind of maintenance, known as corrective, has been complemented gradually by an approach that anticipated failure mechanisms, that is, by a maintenance carried out before the failure occurred. These two vast kinds of maintenance – corrective and preventive – present certain variations described below. Figure 1.1 shows their global structure.

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

1.1.2.2. Corrective and preventive maintenances

The standard EN 13306 (2001) defines corrective maintenance as a “maintenance carried out after fault recognition and intended to put an item into a state in whichit can perform a required function.” [EN 01]. This kind of maintenance is generally suitable in case of equipment for which:

– the consequences of the breakdown are not critical,

– the repairs are easy and does not require a lot of time and

– the investment costs are low.

We can distinguish two forms of corrective maintenance. When the intervention of the maintenance is provisional, we refer to it as “palliative maintenance”. If the works are definitive, we refer to “curative maintenance”.

Preventive maintenance aims to reduce the risks of a failure occurring. The standard EN 13306 (2001) defines it as a “maintenance carried out at predetermined intervals or according to prescribed criteria and intended to reduce the probability of failure or the degradation of the operation of an item.” [EN 01]. When maintenance intervention is performed at fixed and predefined intervals of time, the term “predetermined maintenance” is used. This kind of maintenance is triggered following a schedule (hours of work, kilometers completed, etc.), and is achieved by periodically replacing the parts, without a prior inspection and whatever the degradation state of the goods. Predetermined maintenance can lead to overcare, that is, an excess of useless interventions, and thus financial wastes for the company. In order to remedy this, other forms of preventive maintenance have appeared, based on the monitoring of the actual state of the goods: condition based and predictive maintenances.

1.1.2.3. Condition based and predictive maintenances

Condition-based maintenance is defined as “a preventive maintenance based on performance and/or parameter monitoring and the subsequent actions”. This maintenance strategy is thus based on real-time analysis of data of the industrial equipment (for instance vibrations, temperature, etc.). It aims to detect anomalies in the operation of industrial machinery: the discovery of changes in their characteristics prefigures a future failure in the short term. Condition-based maintenance takes into account the usage conditions of the equipment better than the traditional predetermined maintenance. This said, it does not allow designing the maintenance policy with certainty: the occurrence date of the failure remains uncertain. Predictive maintenance aims to remedy this lack of knowledge. It is defined as “a condition based maintenance carried out following a forecast derived from the analysis and evaluation of the significant parameters of the degradation of the item.” The underlying idea is to project into the future the current state of the good, in order to estimate the operating time before failure. Therefore, predictive maintenance is more dynamic. It takes into the account the current conditions of the equipment and tries to foresee the good’s state evolution in time. As maintenance interventions are planned with precision beforehand, predictive maintenance saves money substantially, and it has been the subject of growing attention for some years now. There are numerous benefits expected from it:

– Reduction of the number of breakdowns

– Increased reliability of production processes

– Improvement of personnel safety and of company image

– Reduction of periods of inactivity for the equipment (costly)

– Increment of the performance of the company.

The implementation of a predictive maintenance policy is based on deployment of a key process targeted at determining the future states of the monitored system: the “industrial prognostics”. The next section is dedicated to this concept.

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

1.2.1. What is it? Definition and measures of prognostics

Many definitions for the term “prognostics” have been proposed in the literature [BYI 02, ENG 00, HEN 09a, HES 05, JAR 06, LEB 01, LUO 03, MUL 05, PRO 03b, SIK 11, VAC 06, WU 07, ZIO 10a]. The different meanings stem essentially from the career and the applicatory sensitivity of the authors. However, although without a total consensus, prognostics can be defined as proposed by the ISO committee:

Standard ISO 13381 (2004).