Machinery Prognostics and Prognosis Oriented Maintenance Management (eBook)

Jihong Yan (Autor)

eBook Download: EPUB

2014 | 1. Auflage
375 Seiten
Wiley (Verlag)
978-1-118-63876-7 (ISBN)

Infos

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.  Latest research results and application methods are introduced for signal processing, reliability moelling, deterioration evaluation, residual life prediction and maintenance-optimization as well as applications of these methods.

Autor

Jihong Yan, Professor and Head of Department of Industrial Engineering, Harbin Institute of Technology, China Professor Yan has been working in the area of intelligent maintenance for over ten years, starting at the Centre for Intelligent Maintenance Systems (IMS) funded by NSF in the US as a researcher for three years, mainly focused on prognosis algorithm development. He then joined Pennsylvania State University in 2004 to work on personnel cross training related topics. From 2005 to the present he is a Professor at Harbin Institute of Technology, China. Professor Yan's research is focused on 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, and maintenance scheduling.

Inhalt

Preface i

Acknowledgements i

Chapter 1 Introduction 7

1.1 Historical perspective 7

1.2 Diagnostic and prognostic system requirements 8

1.3 Need for prognostics and sustainability based maintenance management 9

1.4 Technical challenges in prognosis and sustainability based maintenance decision making 11

1.5 Data processing, prognostics and decision making 13

1.6 Sustainability based maintenance management 16

1.7 Future of prognostics based maintenance 19

References 20

Chapter 2 Data processing 21

2.1 Probability Distributions 21

2.2 Statistics on Unordered data 32

2.3 Statistics on Ordered Data 38

2.4 Technologies for incomplete data 39

References 428

Chapter 3 Signal processing 45

3.1 Introduction 45

3.2 Signal pre-processing 47

3.3 Techniques for signal processing 50

3.4 Real-time image feature extraction 72

3.5 Fusion or integration technologies 77

3.6 Statistical pattern recognition and data mining 80

3.7 Advanced technology for feature extraction 92

References 102

Chapter 4 Health monitoring and prognosis 110

4.1 Health monitoring as a concept 110

4.2 Degradation indices 111

4.3 Real-time monitoring 116

4.4 Failure prognosis 142

4.5 Physics-based prognosis models 155

4.6 Data-driven prognosis models 158

4.7 Hybrid prognosis models 162

Reference 165

Chapter 5 Prediction of residual service life 172

5.1 Formulation of problem 172

5.2 Methodology of probabilistic prediction 173

5.3 Dynamic life prediction using time series 180

5.4 Residual life prediction by crack-growth criterion 197

References 202

Chapter 6 Maintenance planning and scheduling 205

6.1 Strategic planning in maintenance 205

6.2 Maintenance scheduling 219

6.3 Scheduling techniques 232

6.4 Heuristic methodology for multi-unit system maintenance scheduling 261

References 266

Chapter 7 Prognosis incorporating maintenance decision making 270

7.1 The changing role of maintenance 270

7.2 Development of maintenance 272

7.3 Maintenance effects modeling 274

7.4 Modeling of optimization objective - maintenance cost 282

7.5 Prognosis oriented maintenance decision making 284

7.6 Maintenance decision making considering energy consumption 301

References 317

Chapter 8 Case studies 321

8.1 Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis 322

8.2 Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery 329

8.3 BP Neural Networks Based Prognostic Methodology and Its Application 336

8.4 A Dynamic Multi-scale Markov Model Based Methodology for Remaining Life Prediction 343

8.5 A group technology based methodology for maintenance scheduling for hybrid shop 358

References 365

Index 369

Textauszug

Chapter 1
Introduction

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].

Recently, a new generation of maintenance, e-maintenance, is emerging with globalization and fast growth of communication technologies, computer, and information technologies. e-Maintenance is a major pillar in modern industries that supports the success of the integration of e-manufacturing and e-business, by which manufactures and users can benefit from the increased equipment and process reliability with optimal asset performance and seamless integration with suppliers and customers.

1.2 Diagnostic and Prognostic System Requirements

Diagnostics deals with fault detection, isolation, and identification when it occurs. Fault detection is a task to indicate whether something is going wrong in the monitored system; fault isolation is a task to locate the component that is faulty; and fault identification is a task to determine the nature of the fault when it is detected. In recent years, technological development in areas like data mining (DM), data transmission, and databases has provided the technical support for prognostics. Prognostics deals with fault prediction before it occurs. Fault prediction is a task to determine whether a fault is impending and to estimate how soon and how likely it is that a fault will occur. Diagnostics is post-event analysis, and prognostics is prior event analysis. Prognostics is much more efficient than diagnostics in achieving zero-downtime performance. Diagnostics, however, is required when the fault prediction of prognostics fails and a fault occurs.

As a minimum, the basic technical requirements of diagnostics mainly include

1. Sensor location, which has a significant impact on the measurement accuracy.
2. Feature extraction to obtain the parameter which characterizes equipment performance by utilizing signal processing methods including a fast Fourier Transform (FFT) algorithm, a wavelet transform (WT), and so on.
3. Method of fault classification to increase the accuracy of equipment failure classification.

In addition to those technical requirements mentioned above, to specify prognostics accuracy requirements we also need

1. Data on performance degradation, which indicates the decline of equipment performance in the working process.
2. Methods for life prediction to guarantee the safe operation of equipment and improve economic benefits.
3. A confidence interval to estimate the bounds of parameters in the model-based prediction.

Commonly, some aspects of hardware technology, such as the accuracy of sensors, the selection of the location of sensors, and data acquisition provide the technological foundations of prognostics. Also, computer-assisted software techniques, including data transmission, database, and signal processing methods are essential components of a prognostics system.

1.3 Need for Prognostics and Sustainability-Based Maintenance Management

Any organization that owns any large capital assets will eventually face a crucial decision whether to repair or replace those assets, and when. This decision can have far reaching consequences, replacing too early can mean a waste of resources, and replacing too late can mean catastrophic failure. The first is becoming more unacceptable in today's sustainability-oriented society, and the second is unacceptable in the competitive marketplace.

Equipment degradation and unexpected failures impact the three key elements of competitiveness – quality, cost, and productivity [3]. Maintenance has been introduced to reduce downtime and rework and to increase consistency and overall business efficiency. However, traditional maintenance costs constitute a large portion of the operating and overhead expenses in many industries [4]. More efficient maintenance strategies, such as prognostics-based maintenance are being implemented to handle the situation. It is said that prognostics-based maintenance can reduce the maintenance costs by approximately 25% [5]. Generally, machines go through degradation before failure occurs, monitoring the trend of machine degradation and assessing performance allow the degraded behavior or faults to be corrected before they cause failure and machine breakdowns. Therefore, advanced prognostics focuses on performance degradation monitoring and prediction, so that the failures can be predicted and prevented [6].

If large capital assets are analyzed as repairable systems, additional significant information can be incorporated into maintenance optimization models. When these assets break down, but have not yet reached their end-of life, they can be repaired and returned to operating condition. However, sometimes malfunctioning equipment cannot be properly fixed or repaired to its original healthy condition. In this case, the application of prognostics will help solve this problem and avoid irreparable and irreversible damage. Prognostics provides the basic information for a maintenance management system where a maintenance decision is made by predicting the time when the reliability or the remaining life of a facility reaches the maintenance threshold. However, inappropriate maintenance time will result in waste of resources and a heavier environmental load. Nowadays, more efficient maintenance strategies, such as sustainability oriented maintenance management, are put forward. Sustainability-based maintenance management (SBMM) 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. SBMM will be one of the important strategies for sustainable development.

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

In order to implement prognostics, three main steps are needed. (i) Feature extraction and selection: feature extraction is the process of transforming the raw input data acquired from mounted or built-in sensors into a concise representation that contains the relevant information on the health condition. Feature selection is the selection of typical features which reflect an overall degradation trend from the extracted features. (ii)...

Produkt-Details

• Erscheint lt. Verlag: 10.11.2014
• Sprache: englisch
• Themenwelt: Technik ► Maschinenbau
• Schlagworte: Control Process & Measurements • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • Industrial Engineering • Industrial Engineering / Manufacturing • Industrielle Verfahrenstechnik • Maschinenbau • mechanical engineering • Mess- u. Regeltechnik • Produktion i. d. Industriellen Verfahrenstechnik • Prognose • Qualität u. Zuverlässigkeit • Qualität u. Zuverlässigkeit • Quality & Reliability • Technische Zuverlässigkeit • Technische Zuverlässigkeit • Wartung
• ISBN-10: 1-118-63876-X / 111863876X
• ISBN-13: 978-1-118-63876-7 / 9781118638767