Multiwavelet transform and its applications in mechanical fault diagnosis – A review

Sun, Hongjian; He, Zhengjia; Ye, Zi; Yuan, Jing; Wang, Xiaodong; Chen, Jinglong; He, Shuilong

doi:10.1016/j.ymssp.2013.09.015

Cited by 118 publications

(50 citation statements)

References 87 publications

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“…Inspired by the systematic research on WT by Selesnick et al [179][180][181], overcomplete rational dilation discrete WT and tunable Q-factor WT have been studied and applied for bearing and gearbox fault diagnosis [115,[182][183][184]. As opposed to the classical WT using a single wavelet function to capture fault-related features, the multi-wavelet concept offers multiple wavelet functions, thus matching one or more faults for diagnosis [185][186][187][188]. Although the techniques of TFA and WT for mechanical fault diagnosis have been researched for more than two decades, some challenges remain in using TFA and WT for mechanical fault diagnosis.…”

Section: Sparse Decomposition Analysismentioning

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: Sparse Decomposition Analysismentioning

confidence: 99%

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

et al. 2017

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“…based on wavelets, wavelet packets, dual-tree wavelets, etc. [11][12][13][14][15][16][17][18][19]) of the kurtogram in order to improve its efficiency. However, one of the most serious limitations of the kurtogram is its inability to recognize whether a series of transients is repetitive or not.…”

Section: Introductionmentioning

confidence: 99%

The infogram: Entropic evidence of the signature of repetitive transients

Antoni¹

2016

Mechanical Systems and Signal Processing

479

211

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a b s t r a c tA classical symptom of rotating machines faults in vibration signals is the presence of repetitive transients, whose distinctive signature is both impulsive and cyclostationary. Typical approaches for their detection proceed in the time or frequency domains, with tools such as the spectral kurtosis, the kurtogram, or the envelope spectrum. The object of this paper is to extend and somehow connect these concepts in order to capture the signature of repetitive transients in both domains. Motivated by ideas borrowed from the field of thermodynamics where transients are seen as departures from a state of equilibrium, it is proposed to measure the negentropy of the squared envelope (SE) and of the squared envelope spectrum (SES) of the signal. This defines the SE infogram, the SES infogram, and their average which is theoretically maximum for a Dirac comb according to Hirschman's uncertainty principle. It is demonstrated that the joint consideration of the infograms significantly extends the domain of applicability of the kurtogram, in particular to situations corrupted with impulsive noise or when the relaxation time of the transients is low as compared to their rate of repetition. This is illustrated on both synthetic and actual vibration signals. This paper is part of a special issue in honor of Professor Simon Braun and pays tribute to his early contribution to the field of mechanical signature analysis.

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“…Hundreds of papers in this field, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. Space lacks for a detailed description of all these methods, interested readers can refer to some review works in the field of the vibration-based fault detection and diagnosis using the wavelet transform [7], multiwavelet transform [8], empirical model decomposition [9] and time-frequency analysis [10], etc. Moreover, all these fault diagnosis methods mentioned above have been used not only on the test rig of the bearings or gears, but extensively in practical equipments, such as helicopters [11], wind turbine [12][13][14], induction machines [15,16] and permanent magnet machines [17].…”

Section: Introductionmentioning

confidence: 99%