Review
A review on empirical mode decomposition in fault diagnosis of rotating machinery

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Abstract

Rotating machinery covers a broad range of mechanical equipment and plays a significant role in industrial applications. It generally operates under tough working environment and is therefore subject to faults, which could be detected and diagnosed by using signal processing techniques. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been extensively studied and widely applied in fault diagnosis of rotating machinery. Numerous publications on the use of EMD for fault diagnosis have appeared in academic journals, conference proceedings and technical reports. This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics. First, the EMD method is briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of EMD to fault diagnosis of rotating machinery are summarized in terms of the key components, such as rolling element bearings, gears and rotors. Finally, the outstanding open problems of EMD in fault diagnosis are discussed and potential future research directions are identified. It is expected that this review will serve as an introduction of EMD for those new to the concepts, as well as a summary of the current frontiers of its applications to fault diagnosis for experienced researchers.

Highlights

► Recent applications of EMD to fault diagnosis of rotating machinery are summarized. ► The outstanding open problems of EMD in fault diagnosis are discussed. ► This review provides comprehensive references and identifies future topics.

Introduction

Rotating machinery is one of the most common classes of mechanical equipment and plays an important role in industrial applications. It generally operates under tough working environment and is therefore subject to failures, which may cause machinery to break down and decrease machinery service performance such as manufacturing quality, operation safety, etc. With rapid development of science and technology, rotating machinery in modern industry is growing larger, more precise and more automatic. Its potential faults become more difficult to be detected. Therefore, the need to increase reliability against possible faults has attracted considerable interests in fault diagnosis of rotating machinery in recent years. Adopting effective signal processing techniques to analyze the response signals and to reveal fault characteristics is one of the commonly used strategies in fault diagnosis of rotating machinery [1], [2]. However, it is a challenge to develop and adopt effective signal processing techniques that can discover crucial fault information from the response signals [3].

Traditional signal processing techniques, including time-domain and frequency-domain analysis, are based on the assumption that the process generating signals is stationary and linear. They may result in false information once they are applied to mechanical fault signals, as the mechanical faults may be non-stationary and generate transient events [4], [5]. To deal with non-stationary signals, several advanced time-frequency analysis techniques have been introduced and applied to fault diagnosis of rotating machinery [6], [7].

Empirical mode decomposition (EMD) [8] is one of the most powerful time-frequency analysis techniques. It is based on the local characteristic time scales of a signal and could decompose the signal into a set of complete and almost orthogonal components called intrinsic mode function (IMF). The IMFs indicate the natural oscillatory mode imbedded in the signal and serve as the basis functions, which are determined by the signal itself, rather than pre-determined kernels. Thus, it is a self-adaptive signal processing technique that is suitable for nonlinear and non-stationary processes. Since EMD was introduced in 1998, it has been extensively studied and widely utilized in various areas, for example, process control [9], [10], modeling [11], [12], [13], surface engineering [14], medicine and biology [15], voice recognition [16], system identification [17], [18], etc. The number of publications on EMD has been increasing steadily over the past decade.

Since EMD is suitable for processing nonlinear and non-stationary signals, it has attracted attention from researchers in the field of fault diagnosis of rotating machinery as well. Studies on EMD applied to fault diagnosis of rotating machinery grow at a very rapid rate in the past few years. Many publications on this topic, including theory and applications, appear every year in academic journals, conference proceedings and technical reports. Huang and Wu [19] provided a thorough review on EMD and Hilbert–Huang transform applied to geophysical studies, while a survey on the use of EMD to fault diagnosis of rotating machinery has not been reported based on the authors’ literature search.

This paper attempts to summarize and review the recent research and development of EMD in fault diagnosis of rotating machinery. It aims to synthesize and place the individual pieces of information on this topic in context and provide comprehensive references for researchers, helping them develop advanced research in this area. The paper surveys the applications of EMD in fault diagnosis based on the diagnosis objects such as rolling element bearings, gears and rotors, which are the common and key components of rotating machinery. Moreover, for each kind of the diagnosis objects, we review the research in terms of different methodologies, namely the original EMD method, improved EMD methods, EMD combined with other techniques, etc.

The remaining part of the paper is organized as follows. Section 2 introduces the EMD algorithm and its problems, and the EEMD algorithm. Section 3 reviews the applications of EMD to fault diagnosis according to the key components and the methodologies used for each component. Section 4 provides a brief summary by synthesizing the papers in a table and points out some existing problems of EMD in fault diagnosis. Section 5 describes prospects of EMD in fault diagnosis and identifies possible research directions in future. Concluding remarks are given in Section 6.

Section snippets

EMD algorithm

The EMD method was introduced by Huang et al. [8] and is able to decompose a signal into some IMFs. An IMF is a function that satisfies the following two conditions: (1) in the whole data set, the number of extrema and the number of zero-crossings must either equal or differ at most by one, and (2) at any point, the mean value of the envelope defined by local maxima and the envelope defined by the local minima is zero. An IMF represents a simple oscillatory mode imbedded in the signal. Based on

Applications of EMD in fault diagnosis of rotating machinery

Rolling element bearings, gears and rotors are the common and key components in rotating machinery. The health condition of these key components represents that of the machine itself. Hence, this section will present a review on the applications of EMD in fault diagnosis in terms of these key components, i.e. bearings, gears and rotors. Research on other diagnosis objects using EMD is introduced as well at the end of this section.

Discussions

In previous sections, we have summarized reported studies on using EMD in fault diagnosis of rotating machinery. Actually, the literature on this subject is huge and diverse. A review on all of the literature is impossible and omission of some papers would be inevitable. It is believed that the applications of EMD to fault diagnosis of rotating machinery have also been published in other languages as well. However, non-English publications are not considered in this review due to the limitation

Prospects of EMD in fault diagnosis

  • (1)

    As discussed in the previous section, researchers have widely used EMD to detect and diagnose faults of bearings, gears and rotors in rotating machinery. However, one thing that we must keep in mind is that investigation on fault mechanism and dynamic response characteristics of rotating machinery is of primary importance. Therefore, EMD could be applied to fault diagnosis properly instead of blindly only if we thoroughly understand both the fault mechanism and the advantages of EMD in

Concluding remarks

In this paper, we have attempted to provide a review of applying EMD to fault diagnosis of rotating machinery. In the review, all reported applications of EMD in fault diagnosis are divided into a few main aspects based on the key components of rotating machinery, namely, rolling element bearings, gears and rotors. For each component, the review is accomplished following diagnosis methodologies including the original EMD method, improved EMD methods, EMD combined with other techniques, etc.

Acknowledgments

This research is supported by National Natural Science Foundation of China (51005172, 51222503, 51125022), New Century Excellent Talents in University (NCET-11–0421), Natural Sciences and Engineering Research Council of Canada (NSERC), and Fundamental Research Funds for the Central Universities.

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