Extraction of weak crack signals based on sparse code shrinkage combined with wavelet packet filtering

Wang, Xianghong; Luo, Zhizeng; Hu, Hongwei; Mao, Hanling

doi:10.1016/j.apacoust.2016.05.003

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Demodulation methods have been studied extensively in the past few decades, such as envelope analysis, spectral kurtosis (Y. [22,23], cyclostationary analysis [14]. The cyclostationary analysis method is a high order statistical analysis method, which shows good extraction capability of modulation features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The MFBD: a novel weak features extraction method for rotating machinery

Song

Liu

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

For the fault diagnosis of rotating machinery, the demodulation algorithm of the monitoring signals plays a key role in fault feature extraction. Especially for weak fault features extraction, existing single narrow band demodulation methods have worse performance under low signal to noise ratio condition. According to the mechanism of rotating machinery, both narrow and broad frequency band modulated signals exist simultaneously. Therefore, weak fault features can be obtained through demodulation of multiple narrow frequency bands rather than only one resonance narrow band. In this study, a novel weak feature extraction method is proposed based on used as a good filtermultiple frequency bands demodulation. The superiority of the proposed method is corroborated by simulation analysis and applications of a centrifugal pump and a propeller. By simulation analysis, the proposed multiple frequency bands demodulation (MFBD) method has better demodulation performance than Fast Kurtogram, Autogram and Fast-SC for weak modulation features. The applications results suggested that the proposed MFBD provided a clearer characteristic frequency identification than Fast Kurtogram, Autogram and Fast-SC, especially in weak modulation condition. Therefore, the proposed MFBD method provides a reliable basis for weak fault signal extraction, which shows good engineering significance for fault diagnosis of rotating machinery and passive detection of propeller.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The Meyer wavelet packet filter was applied to decompose the monitoring signals and extract weak signals (X. [22,23].…”

Section: Introductionmentioning

confidence: 99%

The MFBD: a novel weak features extraction method for rotating machinery

Song

Liu

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Zhang et al presented a wavelet-based image restoration framework based on group-sparse Gaussian scale mixture model [26]. Aiming at detecting early crack signatures, Wang et al proposed a denoising method combining wavelet packet technology with sparse code shrinkage [27]. According to the state-of-the-art researches in the literature, the utilization of redundancy and the improvement of time-frequency configuration are major trends of development of wavelet theory.…”

Section: Introductionmentioning

confidence: 99%

Fractal Lifting Wavelets for Machine Fault Diagnosis

Chen

Zeng

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Fault diagnosis is of vital importance in safety and reliable operations of modern electromechanical systems. Advanced signal processing techniques are indispensable for extracting incipient features from measured dynamical signals. For discrete wavelet analysis, shift-invariance and proper frequency-scale configuration are both necessary for effective investigation of incipient fault features. In this paper, a novel fractal lifting scheme is proposed based on redundant second generation wavelet packet decomposition (RSGWPD). Implicit wavelet packets (IWPs), generated via fractal lifting scheme, can realize a novel centralized multiresolution. It is demonstrated that each IWP inherits the property of exact shift-invariance originated from redundant lifting scheme. In addition, a novel concept of nested centralized wavelet packet cluster is introduced for explaining merits provided by sets composed of IWPs. The numerical simulations were employed to validate the benefits of exact shift-invariance in a multiscale analysis of discrete time series. RSGWPD and IWPs are combined to conduct multiscale expansion of vibration measurement. To further explore optimal features, an indicator of spatial-spectral ensemble kurtosis is utilized to select optimal analysis parameters. The proposed technique was successfully applied to case studies of gearbox fault diagnosis as well as bearing fault diagnosis. The comparisons were made between results by the proposed technique and those provided by some other mainstream adaptive signal decomposition methodologies. It is verified that the combination of exact shift-invariance and centralized multiresolution significantly enhances the performance of incipient fault feature extraction of nonstationary vibration signals. INDEX TERMS Structural health monitoring (SHM), second generation wavelet transform (SGWT), fractal lifting scheme (FLS), implicit wavelet packet (IWP).

show abstract

“…Lin et al [ 29 ] utilized the maximum-likelihood estimation function of SCS as the soft threshold function of wavelet threshold de-noising for diagnosis pf gear faults, but the diagnosis effort was not ideal under the strong noise interference due to a lack of pre-filtering. Wang et al [ 30 ] put forward an integrated method based on wavelet packet and SCS, which can filter the noise contained in the crack signal, but which needs manual selection of the wavelet packet coefficients to reconstruct the signal for subsequent SCS processing. Yu et al [ 31 ] combined intrinsic time-scale decomposition (ITD) and SCS to diagnose the bearing failure type.…”

Section: Introductionmentioning

confidence: 99%

An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage

Wan

Peng

2019

Entropy

View full text Add to dashboard Cite

Early fault information of rolling bearings is weak and often submerged by background noise, easily leading to misdiagnosis or missed diagnosis. In order to solve this issue, the present paper puts forward a fault diagnosis method on the basis of adaptive frequency window (AFW) and sparse coding shrinkage (SCS). The proposed method is based on the idea of determining the resonance frequency band, extracting the narrowband signal, and envelope demodulating the extracted signal. Firstly, the paper introduces frequency window, which can slip on the frequency axis and extract the frequency band. Secondly, the double time domain feature entropy is proposed to evaluate the strength of periodic components in signal. The location of the optimal frequency window covering the resonance band caused by bearing fault is determined adaptively by this entropy index and the shifting/expanding frequency window. Thirdly, the signal corresponding to the optimal frequency window is reconstructed, and it is further filtered by the sparse coding shrinkage algorithm to highlight the impact feature and reduce the residue noise. Fourthly, the de-noised signal is demodulated by envelope operation, and the corresponding envelope spectrum is calculated. Finally, the bearing failure type can be judged by comparing the frequency corresponding to the spectral lines with larger amplitude in the envelope spectrum and the fault characteristic frequency. Two bearing vibration signals are applied to validate the proposed method. The analysis results illustrate that this method can extract more failure information and highlight the early failure feature. The data files of Case Western Reserve University for different operation conditions are used, and the proposed approach achieves a diagnostic success rate of 83.3%, superior to that of the AFW method, SCS method, and Fast Kurtogram method. The method presented in this paper can be used as a supplement to the early fault diagnosis method of rolling bearings.

show abstract

Extraction of weak crack signals based on sparse code shrinkage combined with wavelet packet filtering

Cited by 5 publications

References 16 publications

The MFBD: a novel weak features extraction method for rotating machinery

The MFBD: a novel weak features extraction method for rotating machinery

Fractal Lifting Wavelets for Machine Fault Diagnosis

An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage

Contact Info

Product

Resources

About