Application of an improved kurtogram method for fault diagnosis of rolling element bearings

Lei, Yaguo; Lin, Jing; He, Zhengjia; Ye, Zi

doi:10.1016/j.ymssp.2010.12.011

Cited by 410 publications

(253 citation statements)

References 14 publications

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“…The Morlet WT has also been investigated as a filter bank to construct an adaptive SK filtering technique in order to extract the signal transients [106]. Considering that wavelet packet transform (WPT) could process nonstationary transient signals more efficiently than STFT, Lei et al [107] replaced STFT with WPT to improve the original kurtogram. Chen et al [5] presented a type of quasi-analytic wavelet tight frame (QAWTF), which is generated from dual-tree complex WT, to replace the multi-rate filter-bank or STFT to map a new kurtogram.…”

Section: Spectral Kurtosismentioning

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: Spectral Kurtosismentioning

confidence: 99%

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

et al. 2017

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“…Kurtogram is a powerful tool for the analysis of nonstationary signals in bearing fault diagnosis, though it has been reported that the technique fails to detect a bearing fault when the defect signal has low signal-to-noise ratio and contains non-Gaussian noise with high peaks [10,11].…”

Section: Spectral Kurtosismentioning

confidence: 99%

Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Lin¹,

Yu²,

Tan³

2017

Bearing Technology

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Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine's operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition.

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“…Lei et al proposed an improved kurtogram method [5], which uses wavelet packet transform (WPT) instead of the short time Fourier transform (STFT) and FIR filters used in the traditional kurtogram. A filter based on WPT can accurately divide the frequency bands and control the noise effectively [5]. Then, on the basis of Lei et al, Wang et al made further improvements.…”

Section: Introductionmentioning

confidence: 99%

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

Wan

Zhang

2018

Entropy

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Abstract:Kurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a) the decomposition method of the frequency band; and (b) the selection index of the optimal frequency band. In this article, a new method called Teager Energy Entropy Ratio Gram (TEERgram) is proposed. The TEER algorithm takes the wavelet packet transform (WPT) as the signal frequency band decomposition method, which can adaptively segment the frequency band and control the noise. At the same time, Teager Energy Entropy Ratio (TEER) is proposed as a computing index for wavelet packet subbands. WPT has better decomposition properties than traditional finite impulse response (FIR) filtering and Fourier decomposition in the kurtogram algorithm. At the same time, TEER has better performance than the envelope spectrum or even the square envelope spectrum. Therefore, the TEERgram method can accurately identify the resonant frequency band under strong background noise. The effectiveness of the proposed method is verified by simulation and experimental analysis.

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Application of an improved kurtogram method for fault diagnosis of rolling element bearings

Cited by 410 publications

References 14 publications

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

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

Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

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