Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis

Zhang, Yi; Lv, Yong; Ge, Mao

doi:10.1016/j.egyr.2021.04.045

Cited by 24 publications

(6 citation statements)

References 47 publications

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“…Since the fault frequency of original signals cannot be accurately identified from the spectrogram, the DMD method is employed to reprocess the signals that were processed by MOMEDA. The CER of the envelope spectrum is taken as the indicator to select the optimal truncation level r f for DMD [39], as exhibited by equation (30). The total energy is counted from 1/2 f i , which can be immune from the influence of strong initial edge frequencies on the energy ratio.…”

Section: Methodology and Flowchartmentioning

confidence: 99%

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

Xiong¹,

Lv²,

Dang³

et al. 2022

Meas. Sci. Technol.

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Fault vibration signals of rolling bearings in early stages are affected by complex transmission paths and strong background noise, resulting in weak information about fault characteristics, which is difficult to extract clearly and accurately. To this end, a new diagnosis method for early faults of rolling bearings is proposed. First, the parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted (PA-MOMEDA) algorithm is used to preprocess the fault signals by strengthening their shock components and weakening the influence of noise on their results. Second, the maximum envelope-spectrum characteristic energy ratio is employed as the selection criterion for the optimal truncation order of dynamic mode decomposition (DMD) to decompose and reconstruct the signals. Finally, the processed signals are subjected to the Hilbert envelope spectral transformation to accurately extract early fault characteristic frequencies. An analysis of simulated signals, public database signals, and bearing signals from a wind turbine has shown that the proposed PA-MOMEDA–DMD method can successfully extract the early fault characteristics of rolling bearings. Compared with the traditional pattern decomposition algorithms, the proposed method is much better at extracting fault characteristics and diagnosing early faults of rolling bearings. The facts have proved that the proposed method is promising in engineering applications.

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Section: Methodology and Flowchartmentioning

confidence: 99%

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

Xiong¹,

Lv²,

Dang³

et al. 2022

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to verify the superiority of the proposed method, some classical methods and the latest proposed methods are compared with aMTSA. NRC [16], the method in [12], autocorrelation denoising, empirical wavelet decomposition [26], minimum entropy deconvolution [27], maximum correlation kurtosis deconvolution [28], and multi-point optimal minimum entropy deconvolution adjustment [29] are compared with aMTSA. To facilitate expression, these methods are expressed as M1, M2, M3, M4, M5, M6, M7, and aMTSA as M8.…”

Section: Comparative Experimentsmentioning

confidence: 99%

An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

Yin¹,

Meng²,

Guan³

et al. 2022

Meas. Sci. Technol.

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The time domain synchronous averaging (TSA) method is a typical time domain signal denoising method, which is widely used in the state detection of rotating machinery. In order to solve the difficult problem of extracting vibration signal features from strong interference, an adaptive multiple time domain synchronous averaging(aMTSA) method based on signal period is proposed in this paper. In view of the blindness and randomness of period selection in TSA method, a new evaluation index of periodic impulse characteristics is proposed. In this method, the signal is resampled then the iteration stop threshold is set, and then the calculation period of interest is determined by two cycle screening. Finally, reconstructed signals with enhanced features are obtained by copying and stitching. Experimental results show that the proposed method is robust and superior in the feature detection of rolling bearing vibration signals.

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“…At the same time, the change of the indicator can reflect the change of the fault degree to achieve the purpose of fault diagnosis. Zhang et al 30 used the proposed enhanced MCKD to extract the characteristics of the composite fault of the wind turbine rolling bearing, and used the spectrum analysis method to diagnose the fault.…”

Section: Introductionmentioning

confidence: 99%

A novel method for early fault diagnosis of planetary gearbox with distributed tooth surface wear

Gao

Shang

et al. 2023

Structural Health Monitoring

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Planetary gearbox (PGB) usually work in harsh working conditions with low speed and heavy load, and they are prone to wear. Different from the local faults, the distributed faults such as tooth surface wear are often weak and difficult to detect in the early stage, and it is difficult to extract fault characteristic. This paper presents an early fault diagnosis method for the distributed tooth surface wear of PGB to solve this problem. The proposed multi-channel optimal maximum correlation kurtosis deconvolution (MCO_MCKD) algorithm is used to extract fault characteristic. In order to enhance the effect of fault characteristic extraction (FCE), the algorithm first uses the sliding window principle to segment the input signal to establishes multiple channels for maximum correlation kurtosis (max_CK) optimization based on all the short signals obtained. The finite impulse response (FIR) filter with the max_CK is selected to filter the input signal, in order to realize FCE. The influence of tooth wear is mainly reflected in the frequency-domain signal amplitude. In order to realize early fault diagnosis, the frequency-domain statistical indicator fault characteristic energy ratio (FCER) is proposed based on this characteristic. The health status of the equipment is monitored by calculating the FCER of the signal after FCE. Early fault diagnosis is realized based on the mutation of the FCER. The simulation results show that MCO_MCKD algorithm has strong robustness. The experimental results show this proposed method is effective and superior.

show abstract

Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis

Cited by 24 publications

References 47 publications

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

A novel method for early fault diagnosis of planetary gearbox with distributed tooth surface wear

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