Teager energy spectral kurtosis of wavelet packet transform and its application in locating the sound source of fault bearing of belt conveyor

Zhang, Xiong; Wan, Shuting; He, Yu‐Ling; Wang, Xiaolong; Dou, Longjiang

doi:10.1016/j.measurement.2020.108367

Cited by 41 publications

(20 citation statements)

References 21 publications

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“…The spectral kurtosis (SK) is a spectral statistical indicator which can detect not only the transient impulses in the presence of strong background noise but also reveal the impulsive components in frequency domain [ 7 , 8 ]. Moreover, the kurtosis of the TES can well reflect periodic impulse signal with a low SNR and is not sensitive to non-periodic transient impulse component [ 35 ]. In Ref.…”

Section: Proposed Methodsmentioning

confidence: 99%

The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis

Yang

2021

Sensors

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Motor bearings are one of the most critical components in rotating machinery. Envelope demodulation analysis has been widely used to demodulate bearing vibration signals to extract bearing defect frequency components but one of the main challenges is to accurately locate the major fault-induced frequency band with a high signal-to-noise ratio (SNR) for demodulation. Hence, an enhanced fault detection method combining the maximal overlap discrete wavelet packet transform (MODWPT) and the Teager energy adaptive spectral kurtosis (TEASK) denoising algorithms is proposed for identifying the weak periodic impulses. The Teager energy power spectrum (TEPS) defines the sparse representation of the filtered signals of the MODWPT in the frequency domain via the Teager energy operator (TEO); the TEASK helps determine the most informative frequency band for demodulation. The methodology is compared in terms of performance with the fast Kurtogram and the Autogram methods. The simulation and practical application examples have shown that the proposed MODWPT-TEASK method outperforms the above two methods in diagnosing defects of motor bearings.

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Section: Proposed Methodsmentioning

confidence: 99%

The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis

Yang

2021

Sensors

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“…Some denoising algorithms, such as the Kalman filter algorithm and wavelet transform filter algorithm, need to set relevant parameters in advance. (2) The essence of SVD is to retain the useful information in the signal subspace and to remove the interference components in the noise subspace. So, the SVD-based denoising algorithm does not introduce additional components into the denoising process.…”

Section: Svd-based Denoising Theorymentioning

confidence: 99%

“…This has led to a considerable amount of research on the vibration-based diagnosis of bearings in the last decades. Many signal processing methods, such as the spectral kurtosis (SK) algorithm [ 2 , 3 ], morphological filter [ 4 ], sparse representation [ 5 ], and time−frequency analysis (TFA) [ 6 , 7 ], have been explored over the years for bearing fault diagnosis. Among them, the TFA techniques can convert the one-dimensional (1D) time-domain signal into a two-dimensional (2D) time−frequency (TF) feature distributed along the time and frequency directions.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

Deng

Liu

et al. 2021

Sensors

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Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert−Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.

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“…For example, Lei et al 9 take advantage of the wavelet packet transform (WPT) in frequency band division (FBD) to exploit the improved kurtogram. Based on this work, Wang et al 10 used the envelope power spectrum kurtosis and Zhang et al 11 constructed the Teager energy kurtosis to select the optimal node of WPT. However, the kurtosis mainly reflects the strength of transient impact, but the cyclic characteristics of transient impact cannot be considered.…”

Section: Introductionmentioning

confidence: 99%

An optimized variational mode extraction method for rolling bearing fault diagnosis

Pang

Nazari

Sun

et al. 2021

Structural Health Monitoring

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The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

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Teager energy spectral kurtosis of wavelet packet transform and its application in locating the sound source of fault bearing of belt conveyor

Cited by 41 publications

References 21 publications

The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis

The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis

A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

An optimized variational mode extraction method for rolling bearing fault diagnosis

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