Machinery Prognostics and Prognosis Oriented Maintenance Management

Jihong Yan

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9781118638729
• eBook ISBN: 9781118638767
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Quality Control in Engineering
• Index: Technology & Engineering

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

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) Performance assessment: how to effectively evaluate the performance based on the selected features is crucial to prognostics. A good performance assessment method ought to be capable of fusing different information on multiple features for system degradation assessment. (iii) Remaining life prediction: this is a process using prediction models to forecast future performance and obtain the residual useful life of machinery. Remaining life prediction is the most important step in prognostics; it appears to be a hot issue attracting the most attention.

The key point in carrying out intelligent ...

Table of contents

• Cover
• Title Page
• Copyright
• About the Author
• Preface
• Acknowledgements
• Chapter 1: Introduction
• Chapter 2: Data Processing
• Chapter 3: Signal Processing
• Chapter 4: Health Monitoring and Prognosis
• Chapter 5: Prediction of Remaining Useful Life
• Chapter 6: Maintenance Planning and Scheduling
• Chapter 7: Prognosis Incorporating Maintenance Decision-Making
• Chapter 8: Case Studies
• Index
• End User License Agreement