Application of the EEMD method to rotor fault diagnosis of rotating machinery

Lei, Yaguo; He, Zhengjia; Ye, Zi

doi:10.1016/j.ymssp.2008.11.005

Cited by 495 publications

(268 citation statements)

References 18 publications

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“…Li and Wang [196] summarized the development and application of HHT for solving the problem of rolling bearing fault diagnosis from several aspects. Lei et al [197] introduced the enhanced empirical mode decomposition (EEMD) for fault diagnosis of rotating machineries, in which the problem of the and "high-pressure rotor", respectively. The reconstructed signal shows the evidence for vibration jumping fault in the engine, as indicated by the arrow T1 [163] mixing modes is partially solved by adding white noise to the original signal.…”

Section: Emd Lmd and Vmdmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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Section: Emd Lmd and Vmdmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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“…The technical details of the EEMD can be found in Refs. [17,18]. Thus, the original feature set consists of 95 features.…”

Section: Tkdmentioning

confidence: 99%

Fault Diagnosis and Health Assessment for Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence

Yu¹,

Yan²,

Zhang³

2017

Fault Diagnosis and Detection

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To avoid severe damages and unexpected shutdowns, fault diagnosis and health assessment of rotating machinery have received considerable attention in recent years. On the other hand, as a great amount of data become acquirable and accessible in industry, data-driven tools have become an emerging research area, acting as a complement to the model-based (or physics-based) fault diagnosis and health assessment methods. In this chapter, based on the kernel density estimation (KDE) and the Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis approach and a new health assessment approach are introduced. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions, whereas the KLID is able to quantify the discrepancy between two probability distributions of selected features. An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault types or health status of rotating machinery. The effectiveness of the proposed approaches is demonstrated through three case studies of fault diagnosis and health assessment of rotating machinery.

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“…Good diagnosis performances of EMD are reported in [24,25]. In this section, EMD and EMD entropy are introduced and utilized to detect the open switch fault.…”

Section: Empirical Mode Decomposition Shannon Entropy and Tsallis Entmentioning

confidence: 99%