Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

Ge, Mao; Lv, Yong; Ma, Yubo

doi:10.1109/access.2020.3027029

Cited by 8 publications

(6 citation statements)

References 50 publications

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“…Tensor Singular Value Decomposition (Tensor SVD) [34], Generalized Nonconvex Regularization Tensor Robust Principal Component Analysis (GNCTRPCA) [24] and Empirical Fourier Decomposition (EFD) [35,36]. These methods are applied to the same signal for fault information separation.…”

Section: Case 1: Locomotive Bearing With Inner Race Faultmentioning

confidence: 99%

“…Overall speaking, traditional LRSD methods typically deal with each channel of a multichannel signal individually, such practice may ignore joint information between channels and lead to missed diagnosis. Although some studies [24,25] have attempted to expand LRSD in multichannel scenarios, further research is still needed to enhance the separation performance of fault information.…”

Section: Introductionmentioning

confidence: 99%

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Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Ou,

Zhao,

et al. 2024

Meas. Sci. Technol.

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Properly separating fault information from noisy measured signals is crucial for effective bearing health sensing. However, conventional fault information separation methods face challenges such as predefined model parameters and poor noise robustness. Additionally, with the advent of Industry Big Data, multichannel monitoring signals present significant challenges for traditional single decomposition approaches. To address these challenges and fully extract potential fault information, this paper introduces a tensor low-rank and sparse decomposition (tensor LRSD) approach for multichannel signal processing. Inspired by matrix LRSD, we construct a tensor LRSD model that adaptively decomposes the signal into a tensor sparse term containing fault information and a low-rank term representing the intrinsic signal pattern. To further enhance the decomposition performance, a maximum correlation-based selection strategy is designed. This strategy evaluates the correlation between each tensor slice and selects appropriate tensor sparse terms for fault information extraction. Simulation analysis and two experimental studies involving typical bearing failures are implemented to verify the capability and superiority of the presented tensor LRSD approach. The consequences demonstrate that the presented method outperforms conventional techniques, showcasing its capability to effectively separate fault information from noisy signals.

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Section: Case 1: Locomotive Bearing With Inner Race Faultmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Ou,

Zhao,

et al. 2024

Meas. Sci. Technol.

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“…In this section, we firstly introduce the conjugate direction in the optimization problem, and then a conjugate gradient algorithm is derived to extract one signal source through optimizing the CCA criterion equation (3). Finally, we summarize the proposed optimization algorithm, and discuss how to determine the time delay τ to apply the algorithm to the compound fault diagnosis of rolling bearing.…”

Section: Blind Cca Extraction Algorithm Based On Conjugate Gradientmentioning

confidence: 99%

Compound Fault Diagnosis of Aero-Engine Rolling Element Bearing Based on CCA Blind Extraction

Zhang¹,

Huang

et al. 2021

IEEE Access

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Fault diagnosis of aero-engine spindle bearing is a critical technique of engine prognostics and health management. As is known that the diagnosis of compound fault of aero-engine spindle bearing is very difficult and easily affected by other vibration interference signals. We present a CCA criterion based method for blind extraction of specific fault signal from multi-channel observations, which is applicable to compound fault diagnosis of aero-engine spindle bearing. The proposed method uses the different fault characteristic frequency of rolling element bearing to estimate the delay parameter in CCA criterion. The conjugate gradient method is adopted to optimize the CCA criterion, which not only speed up the convergence of the optimization algorithm, but also improves the reliability of the resulted algorithm. Both the simulated data and the experimental data are used to verify the effectiveness of the algorithm in compound fault diagnosis.

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“…TSVD-based methods have achieved excellent applications in image denoising and data recovery, and TSVD is theoretically fully applicable to the processing of mechanical fault signals. Ge et al [ 36 ] studied tensor robust principal component analysis (TRPCA) based on TSVD and achieved good applications in bearing fault diagnosis. Therefore, the TSVD-related method provides a promising way for multichannel signal processing in the tensor domain.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings

Jiang,

Wu,

Yuan

et al. 2024

Sensors

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Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.

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Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 8 publications

References 50 publications

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Compound Fault Diagnosis of Aero-Engine Rolling Element Bearing Based on CCA Blind Extraction

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings

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