Fractional iterative variational mode decomposition and its application in fault diagnosis of rotating machinery

Du, Xiaowei; Wen, Guangrui; Liu, Dan; Chen, Xueyao; Zhang, Yang; Luo, Jianqing

doi:10.1088/1361-6501/ab3361

Cited by 8 publications

(4 citation statements)

References 37 publications

(61 reference statements)

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“…As a high-resolution and fine adaptive filtering method, FRFT makes use of the characteristics of different energy concentrations of non-stationary signals in the fractional order domain of different orders. So that the micro fault has the best energy aggregation in the fractional order domain, while other components and noise will not gather in the fractional order domain [17,18]. The weak fault feature information can be extracted by narrow-band masking in the fractional order domain.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Wang

2022

Symmetry

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Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability.

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Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Wang

2022

Symmetry

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“…Hitherto, numerous interesting studies have been conducted on this issue and a large number of advanced methods, including stochastic resonance [5][6][7], mode decomposition [8][9][10], wavelet denoising [11,12], and spectral kurtosis [13,14] have emerged. Without any doubt, among the various examples, wavelet denoising is one of the most studied and applied techniques, because the signal of interest and the noise often exhibit different characteristics in the wavelet domain and it is possible to discriminate them by processing the wavelet coefficients.…”

Section: Introductionmentioning

confidence: 99%

Sparse representations for fault signatures via hybrid regularization in adaptive undecimated fractional spline wavelet transform domain

Xiong¹,

Zhang²,

Wang³

et al. 2021

Meas. Sci. Technol.

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Recent studies on vibration-based machine diagnostics have highlighted the role played by the wavelet transform (WT). However, common WT-based denoising methods (e.g. wavelet thresholding and non-penalty regularization) are often challenging in attaining accurate sparse representations of fault signatures in practice due to artifacts. In this paper, a method via hybrid regularization in the adaptive undecimated fractional spline WT (AUFrSWT) domain is introduced to achieve the accurate sparse representations of fault signatures from strong noise environments. The method promotes wavelet sparsity by two aspects: wavelet basis design and wavelet coefficient processing. For the former, a new wavelet family, i.e. a fractional spline wavelet, is used, and the AUFrSWT is originally proposed to customize the optimal wavelet basis and meanwhile address the translation-invariant issue. For the latter, the wavelet coefficients are estimated by minimizing a single convex model function with hybrid regularization, where nonconvex arctangent penalty is employed for wavelet sparsity promoting and total variation is used to improve the reconstruction performance of the shrunken coefficients. The method is validated by the analysis of actual bearing and gear vibration data from fault-injection experiments. Analysis and comparison results show great potential for signal sparse representations and machinery fault diagnostics.

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“…Fourier transform and optimized LightGBM were combined in rotating machinery fault diagnosis [21]. Fractional iterative variational mode decomposition was used to decompose the multicomponent linear frequency modulation signal [22]. Zhang et al [23] proposed the moving average to improve TSA and detect the gear fault.…”

Section: Introductionmentioning

confidence: 99%

Improved time domain synchronous averaging based on the moving interpolation and kurtosis criterion searching

Huang¹,

Sun²,

Wei³

et al. 2021

Meas. Sci. Technol.

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The fault diagnosis of rotating machinery involves revealing the possible faults in advance to reduce dispensable breakdowns; the difficulty of this lies in identifying the periodic features of rotating machines contaminated by background noise. Time-domain synchronous averaging (TSA) has been studied to eliminate random noise in signals. However, TSA sometimes cannot extract useful signals more accurately because of the accumulative phase error caused by the discrete sampling process and the difficulties in obtaining accurate prior information. Hence, the moving interpolation and Kurtosis searching criterion are used for more accurate extraction of harmonics and transient impacts. Also, an improved compensation algorithm based on moving interpolation is proposed to overcome the amplitude attenuation caused by cumulative phase error for low signal to noise ratio (SNR) signal. To determine some parameters in the algorithm such as the number of periods and the time delay of windows which depend on a priori information relevant to the fault period, a searching method for the prior information in vibration signals including transient impacts and harmonics with Kurtosis and minimum mean square error criterion is proposed to optimize the algorithm in the process of feature extraction. Finally, the improved TSA (ITSA) is applied to extracting the fault features in a real factory, and the performance of fault feature extraction in low SNR signal conditions with the ITSA has been enhanced effectively.

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Fractional iterative variational mode decomposition and its application in fault diagnosis of rotating machinery

Cited by 8 publications

References 37 publications

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Sparse representations for fault signatures via hybrid regularization in adaptive undecimated fractional spline wavelet transform domain

Improved time domain synchronous averaging based on the moving interpolation and kurtosis criterion searching

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