Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis

Zhao, Zhibin; Wu, Shuming; Qiao, Baijie; Wang, Shibin; Chen, Xuefeng

doi:10.1109/tie.2018.2838070

Cited by 176 publications

(52 citation statements)

References 46 publications

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“…Group sparse property: Another assumption believes that the periodic fault feature has the group sparse property, which were confirmed by the researches from Ref. [ 46 , 47 , 48 ]. On this basis, several denoising frameworks were designed for bearing fault feature detection.…”

Section: Proposed Slrgsd Framework For Bearing Fault Diagnosismentioning

confidence: 77%

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Zheng

Yin

Xiong

et al. 2020

Sensors

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Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

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Section: Proposed Slrgsd Framework For Bearing Fault Diagnosismentioning

confidence: 77%

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Zheng

Yin

Xiong

et al. 2020

Sensors

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“…However, Zhang et al pointed out that the model‐based approaches had a depressing trouble to obtain an exactly dynamic model of the actual fault signals from complex nonlinear mechanical systems. Meanwhile, the performance of model‐based approaches degraded when the selected parameters of the dynamic model did not match the fault features …”

Section: Data‐driven Structured Sparsity Dictionary Learningmentioning

confidence: 99%

“…Meanwhile, the performance of model-based approaches degraded when the selected parameters of the dynamic model did not match the fault features. 31,32 Therefore, a new sparse model with a data-driven method is constructed in this paper. In our model, a data-driven sparse learning method is employed to overcome the shortcoming of the model-based approach, which depends on the model parameters heavily.…”

mentioning

confidence: 99%

Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine

et al. 2019

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With the increase of the wind turbine capacity, failures occur on the drivetrain of wind turbines frequently. Since faults of bearings in the wind turbine can lead to long downtime and even casualties, fault diagnosis of the drivetrain is very important to reduce the maintenance cost of the wind turbine and improve economic efficiency. However, the traditional diagnosis methods have difficulty in extracting the impulsive components from the vibration signal of the wind turbine because of heavy background noise and harmonic interference. In this paper, we propose a novel method based on data‐driven multiscale dictionary construction. Firstly, we achieve the useful atom through training the K‐means singular value decomposition (K‐SVD) model with a standard signal. Secondly, we deform the chosen atom into different shapes and construct the final dictionary. Thirdly, the constructed dictionary is used to sparsely represent the vibration signal, and orthogonal matching pursuit (OMP) is performed to extract the impulsive component. The proposed method is robust to harmonic interference and heavy background noise. Moreover, the effectiveness of the proposed method is validated by numerical simulation and two experimental cases including the bearing fault of the wind turbine generator in the field test. The overall results indicate that compared with traditional methods, the proposed method is able to extract the fault characteristics from the measured signals more efficiently.

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“…Meanwhile, replacing the popular convex 1 norm regularization, non-convex sparsity-promoting regularization more recently has been extensively studied [27]. Zhao et al proposed an adaptive enhanced sparse period-group lasso model which can promote the sparsity within and across groups of the impulsive bearing fault feature [28]. Wang proposed a nonconvex sparse regularization method based on the generalized minimax convex penalty [29].…”

Section: Introductionmentioning

confidence: 99%

A Bi-Level Nested Sparse Optimization for Adaptive Mechanical Fault Feature Detection

et al. 2020

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Denoising is a permanent topic and there are various denoisers proposed in the fault diagnosis of industrial systems. However, it is still ambiguous to evaluate their performance quantitatively in terms of mean square error (MSE) and further achieve their maximum gains, because it is always infeasible to obtain the MSE metric without real feature signals in the engineering practices. Therefore, leveraging Stein Unbiased Risk Estimator (SURE) theory, a bi-level nested sparse optimization framework (BiNSOF) is proposed to jointly optimize a parameterized sparse denoiser as well as its regularization parameter, further obtaining the near-optimal fault features with a minimum MSE. The inner level of BiNSOF utilizes a 1 regularized sparse denoiser to describe the intrinsic sparse structure of feature information, which can be effectively addressed by popular primal-dual splitting schemes. The core of the outer optimization level is a SURE-based unbiased estimator for MSE, and the minimum MSE search problem is transformed into a quadratic optimization problem which could be fast solved by classic golden section search schemes. The proposed BiNOSP can perfectly approximate the oracle MSE without any real feature information, and further provides a reliable way to obtain the optimal hyper-parameter sets for the maximum performance gains of the sparse denoiser. The computational complexity of the advocated approach is also investigated. Moreover, its feasibility and performances are profoundly evaluated by a set of comprehensive numerical studies. Lastly, two bearing fault detection cases confirm the applicability and superiority of the proposed framework. INDEX TERMS Sparse optimization, Stein unbiased risk estimator (SURE), fault feature detection, primaldual splitting, bi-level nested optimization, adaptive parameter selection.

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Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis

Cited by 176 publications

References 46 publications

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine

A Bi-Level Nested Sparse Optimization for Adaptive Mechanical Fault Feature Detection

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