Machinery Prognostics and Prognosis Oriented Maintenance Management E-Book

Jihong Yan

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

Seitenzahl: 500

Veröffentlichungsjahr: 2014

Beschreibung

This book gives a complete presentatin of the basic essentials of machinery prognostics and prognosis oriented maintenance management, and takes a look at the cutting-edge discipline of intelligent failure prognosis technologies for condition-based maintenance. * Presents an introduction to advanced maintenance systems, and discusses the key technologies for advanced maintenance by providing readers with up-to-date technologies * Offers practical case studies on performance evaluation and fault diagnosis technology, fault prognosis and remaining useful life prediction and maintenance scheduling, enhancing the understanding of these technologies * Pulls togeter recent developments and varying methods into one volume, complemented by practical examples to provide a complete reference

Leseprobe

Table of Contents

Title Page

Copyright

About the Author

Preface

Acknowledgements

Chapter 1: Introduction

1.1 Historical Perspective

1.2 Diagnostic and Prognostic System Requirements

1.3 Need for Prognostics and Sustainability-Based Maintenance Management

1.4 Technical Challenges in Prognosis and Sustainability-Based Maintenance Decision-Making

1.5 Data Processing, Prognostics, and Decision-Making

1.6 Sustainability-Based Maintenance Management

1.7 Future of Prognostics-Based Maintenance

References

Chapter 2: Data Processing

2.1 Probability Distributions

2.2 Statistics on Unordered Data

2.3 Statistics on Ordered Data

2.4 Technologies for Incomplete Data

References

Chapter 3: Signal Processing

3.1 Introduction

3.2 Signal Pre-Processing

3.3 Techniques for Signal Processing

3.4 Real-Time Image Feature Extraction

3.5 Fusion or Integration Technologies

3.6 Statistical Pattern Recognition and Data Mining

3.7 Advanced Technology for Feature Extraction

References

Chapter 4: Health Monitoring and Prognosis

4.1 Health Monitoring as a Concept

4.2 Degradation Indices

4.3 Real-Time Monitoring

4.4 Failure Prognosis

4.5 Physics-Based Prognosis Models

4.6 Data-Driven Prognosis Models

4.7 Hybrid Prognosis Models

References

Chapter 5: Prediction of Remaining Useful Life

5.1 Formulation of Problem

5.2 Methodology of Probabilistic Prediction

5.3 Dynamic Life Prediction Using Time Series

5.4 Remaining Life Prediction by the Crack-Growth Criterion

References

Chapter 6: Maintenance Planning and Scheduling

6.1 Strategic Planning in Maintenance

6.2 Maintenance Scheduling

6.3 Scheduling Techniques

6.4 Heuristic Methodology for Multi-unit System Maintenance Scheduling

References

Chapter 7: Prognosis Incorporating Maintenance Decision-Making

7.1 The Changing Role of Maintenance

7.2 Development of Maintenance

7.3 Maintenance Effects Modeling

7.4 Modeling of Optimization Objective – Maintenance Cost

7.5 Prognosis-Oriented Maintenance Decision-Making

7.6 Maintenance Decision-Making Considering Energy Consumption

References

Chapter 8: Case Studies

8.1 Improved Hilbert–Huang Transform Based Weak Signal Detection Methodology and Its Application to Incipient Fault Diagnosis and ECG Signal Analysis

8.2 Ant Colony Clustering Analysis Based Intelligent Fault Diagnosis Method and Its Application to Rotating Machinery

8.3 BP Neural Networks Based Prognostic Methodology and Its Application

8.4 A Dynamic Multi-Scale Markov Model Based Methodology for Remaining Life Prediction

8.5 A Group Technology Based Methodology for Maintenance Scheduling for a Hybrid Shop

References

Index

End User License Agreement

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Guide

Cover

Table of Contents

preface

Begin Reading

List of Illustrations

Figure 1.1

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Figure 2.1

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List of Tables

Table 2.1

Table 3.1

Table 3.2

Table 3.3

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Table 4.1

Table 4.2

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Table 4.6

Table 5.1

Table 6.1

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Table 8.1

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Table 8.7

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Table 8.14

MACHINERY PROGNOSTICS AND PROGNOSIS ORIENTED MAINTENANCE MANAGEMENT

Jihong Yan

Harbin Institute of Technology, P.R.China

This edition first published 2015

© 2015 John Wiley & Sons Singapore Pte. Ltd.

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Library of Congress Cataloging-in-Publication Data

Yan, Jihong.

Machinery prognostics and prognosis oriented maintenance management / Jihong Yan.

pages cm

Includes bibliographical references and index.

ISBN 978-1-118-63872-9 (hardback)

1. Machinery—Maintenance and repair. 2. Machinery—Service life. 3. Machinery—Reliability. I. Title.

TJ174.Y36 2014

621.8′16—dc23

2014022259

About the Author

Jihong Yan is a full-time Professor (since 2005) in Advanced Manufacturing at Harbin Institute of Technology (HIT), China and is head of the Department of Industrial Engineering, who received her Ph.D. degree in Control Engineering from HIT in 1999. Professor Yan has been working in the area of intelligent maintenance for over 10 years, starting from 2001 when she worked for the Centre for Intelligent Maintenance Systems (IMS) funded by NSF in the US as a researcher for 3 years, mainly focused on prognosis algorithm development and application. Then she joined Pennsylvania State University in 2004 to work on personnel working performance related topics. As a Principal Investigator, she has worked on and completed more than 10 projects in the maintenance-related area, funded by the NSF of China, National High-tech “973” project, the Advanced Research Foundation of the General Armament Department, the Astronautics Supporting Technology Foundation, High-tech funding from industries, and so on. Specifically, her research is focused on the area of advanced maintenance of machinery, such as online condition monitoring, signal data pre-processing, feature extraction, reliability and performance evaluation, fault diagnosis, fault prognosis and remaining useful life prediction, maintenance scheduling, and sustainability-based maintenance management. She has authored and co-authored over 80 research papers and edited 2 books.

Preface

Prognostics-based maintenance, which is a typical pattern of predictive maintenance (PdM) has been developed rapidly in recent years. Prognosis, which is defined as a systematic approach that can continuously track health indicators to predict risks of unacceptable behavior over time, can serve the purpose of assessing the degradation of a facility's quality based on acquired online condition monitoring data. The existing prognostics models can be divided into two main categories, mechanism-based models and data-driven models. Although the real-life system mechanism is often too stochastic and complex to model, a physics-based model might not be the most practical solution. Artificial intelligence based algorithms are currently the most commonly found data-driven technique in prognostics research.

Prognostics provides the basic information for a maintenance management system where the maintenance decision is made by predicting the time when reliability or remaining life of a facility reaches the maintenance threshold. However, inappropriate maintenance time will result in waste of energy and a heavier environmental load. Nowadays, more efficient maintenance strategies, such as sustainability-oriented maintenance management are put forward. Sustainability-based maintenance management not only benefits manufacturers and customers economically but also improves environmental performance. Therefore, from both environmental and economic perspectives, improving the energy efficiency of maintenance management is instrumental for sustainable manufacturing. Sustainability-based maintenance management will be one of the important strategies for sustainable development.

This book aims to present a state-of-the-art survey of theories and methods of machinery prognostics and prognosis-oriented maintenance management, and to reflect current hot topics: feature fusion, on-line monitoring, residual life prediction, prognosis-based maintenance and decision-making, as well as related case studies.

The book is intended for engineers and qualified technicians working in the fields of maintenance, systems management, and shop floor production line maintenance. Topics selected to be included in this book cover a wide range of issues in the area of prognostics and maintenance management to cater for all those interested in maintenance, whether practitioners or researchers. It is also suitable for use as a textbook for postgraduate programs in maintenance, industrial engineering, and applied mathematics.

This book contains eight chapters covering a wide range of topics related to prognostics and maintenance management, and is organized as introduced briefly below.

Chapter 1 presents a systems view of prognostic- and sustainability-based maintenance management.

Chapter 2 introduces widely used probability distribution functions, such as uniform distribution, geometric distribution, normal distribution, and binomial distribution, for processing discrete data, and is illustrated with several examples.

Chapter 3 presents a systematic and in-depth study of signal processing and the application to mechanical condition monitoring and fault identification.

Chapter 4 introduces the reader to the health monitoring concept. In addition, the degradation process, the main parts of a typical real-time monitoring system, and fault prognosis and the methods for remaining useful life prediction are discussed.

Chapter 5 addresses different prediction methods in machine prognosis.

Chapter 6 focuses on maintenance planning and scheduling techniques, including maintenance scheduling modeling, grouping technology (GT) based maintenance, and so on.

Chapter 7 provides an overview of prognosis-oriented maintenance decision-making issues and shows how the prognosis plays an important role in the development of maintenance management.

Chapter 8 presents five significant case studies on prognostics and maintenance management to demonstrate the application of the contents of the previous chapters. These are extracted from some published papers of the author's research group.

This book is a valuable addition to the literature and will be useful to both practitioners and researchers. It is hoped that this book will open new views and ideas to researchers and industry on how to proceed in the direction of sustainability-based maintenance management. I hope the readers find this book informative and useful.

Jihong YanHarbin, ChinaMarch 2014

Acknowledgements

I wish to thank specific people and institutions for providing help during 2013–2014, making the publication of this book possible. I would like to acknowledge the contributors for their valuable contributions. This book would not have been possible without their enthusiasm and cooperation throughout the stages of this project. I also would like to express my gratitude to all the reviewers who improved the quality of this book through their constructive comments and suggestions. Also, I want to thank my students Lin Li, Chaozhong Guo, Lei Lu, Fenyang Zhang, Weicheng Yang, Bohan Lv, Jing Wen, Yue Meng, Chunhua Feng, and Dongwei Liu for editing and typing the manuscript.

The work presented in this book is funded by the National Science Foundation of China (#70971030, #71271068).

Finally, I would like to express my gratitude to my family, especially my little son Richard, for their patience, understanding, and assistance during the preparation of this book. Work on this book has sometimes been at the expense of their time.

Chapter 1Introduction

1.1 Historical Perspective

With the rapid development of industrial technology, machine tools have become more and more complex in response to the need for higher production quality. While a significant increase in failure rate due to the complexity of machine tools is becoming a major factor which restricts the improvement of production quality and efficiency.

Before 1950, maintenance was basically unplanned, taking place only when breakdowns occurred. Between1950 and 1960, a time-based preventive maintenance (PM) (also called planned maintenance) technique was developed, which sets a periodic interval to perform PM regardless of the health status of a physical asset. In the later 1960s, reliability centered maintenance (RCM) was proposed and developed in the area of aviation. Traditional approaches of reliability estimation are based on the distribution of historical time-to-failure data of a population of identical facilities obtained from in-house tests. Many parametric failure models, such as Poisson, exponential, Weibull, and log-normal distributions have been used to model machine reliability. However, these approaches only provide overall estimates for the entire population of identical facilities, which is of less value to an end user of a facility [1]. In other words, reliability reflects only the statistical quality of a facility, which means it is likely that an individual facility does not necessarily obey the distribution that is determined by a population of tested facilities of the same type. Therefore, it is recommended that on-line monitoring data should also be used to reflect the quality and degradation severity of an individual facility more specifically.

In the past two decades, the maintenance pattern has been developing in the direction of condition-based maintenance (CBM), which recommends maintenance actions based on the information collected through on-line monitoring. CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behavior of a physical asset. A CBM program, if properly established and effectively implemented, can significantly reduce maintenance cost by eliminating the number of unnecessary scheduled PM operations.

Prognostics-based maintenance, which is a typical pattern of predictive maintenance (PdM) has been developed rapidly in recent years. Despite the fact that fault diagnosis and prediction are related to the assessment of the status of equipment, and generally considered together, the goals of the decision-making are obviously different. The diagnosis results are commonly used for passive maintenance decision-making, but the prediction results are used for initiative maintenance decision-making. Its goal is minimum use risk and maximum life. By means of fault prediction, the opportune moment from initial defect to functional fault could be estimated. The failure rate of the whole system or some of the components can be modified, so prognostic technology has become a hot research issue. Now fault prediction techniques are classified into three categories according to the recent literature: failure prediction based on an analytical model, failure prediction based on data, and qualitative knowledge-based fault prediction. Artificial-intelligence-based algorithms are currently the most commonly found data-driven technique in prognostics research [1, 2].