Multiple Discolored Cyclic Harmonic Ratio Diagram Based on Meyer Wavelet Filters for Rotating Machine Fault Diagnosis

Luo, Chong; Mo, Zhenling; Wang, Yunlong; Jiang, Jia; Dai, Wenxin; Miao, Qiang

doi:10.1109/jsen.2019.2957413

Cited by 20 publications

(3 citation statements)

References 41 publications

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“…In place of fast Kurtogram, the Meyer wavelet filters of empirical wavelet transform were used owing to the advantages in spectrum separation and localization of time and frequency. The effectiveness and feasibility of the method are demonstrated on planetary gearbox and rolling bearing [88].…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.

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Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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“…In the past few decades, incipient fault diagnosis on rolling bearings has attracted the extensive attention of many scholars, who have carried out a series of studies for guiding industrial applications [13][14][15]. The popular and powerful signal-processing algorithms in bearing fault diagnosis mainly include spectral kurtosis (SK), deconvolution algorithms, and decomposition methods.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Modified Adaptive Chirp Mode Decomposition Method and Its Application on Fault Diagnosis of Wind Turbine Bearings

et al. 2022

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Wind turbine bearings usually work with strong background noise, making the faulty properties difficult to extract and detect. To accurately diagnose the faults of rolling bearings in wind turbines, an iterative modified adaptive chirp mode decomposition (IMACMD) method is proposed in this paper. Firstly, an envelope interpolation method is employed to preliminarily determine the iterative mode number and guide the potentially initial frequency selection. Secondly, the upper limits of the iterative mode number and the initial frequency are further determined through correlation analysis. During the iteration process, the optimal weight factor of the reconstructive input signal, which is the residual signal of the previous iterative decomposition, is determined according to the new designed ensemble L-kurtosis index. Experimental and engineering signals are used to validate the proposed IMACMD method. Comparisons with the conventional methods demonstrate the superiority of this proposed method. It is shown that this method can not only identify the weak features for single faults but also separate the multiple features for compound faults.

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“…Luo et al. 14 proposed to use EWT filters to replace FIR filters in fast kurtogram. More recently, optimization algorithm-based EWT proposed for bearing fault diagnosis 15,16 motivates this paper to provide an alternative solution to address the drawbacks of conventional EWT and extend EWT of such kind to rotating machinery fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

Zhang

Wang³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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Meyer wavelet filters are the key building blocks of empirical wavelet transform. In mechanical fault diagnosis, however, the boundaries of Meyer wavelet filters are usually defined empirically. In order to solve the problems, this paper proposes a new index called harmonic infinite-taxicab norm to guide grasshopper optimization algorithm to primarily optimize a band-pass filter and thus, concurrently and secondarily optimize a low-pass filter and a high-pass filter of Meyer wavelet. The proposed index is inspired by spectral Lp/Lq norm and it is closely related to fault characteristic frequency of rotating machinery. In addition, only three Meyer wavelet filters are demanded in each iteration of optimization. The effectiveness of the proposed method is validated by comparing with fast kurtogram method on analyzing faulty bearing data and gearbox data.

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Multiple Discolored Cyclic Harmonic Ratio Diagram Based on Meyer Wavelet Filters for Rotating Machine Fault Diagnosis

Cited by 20 publications

References 41 publications

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

An Iterative Modified Adaptive Chirp Mode Decomposition Method and Its Application on Fault Diagnosis of Wind Turbine Bearings

Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

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