Fine‐Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Sun, Guodong; Gao, Yuan; Lin, Kai Yan; Hu, Ye

doi:10.1155/2019/1531079

Cited by 15 publications

(10 citation statements)

References 22 publications

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“…Reference [27] directly adopted NMFSC + NLE to obtain the sparse coding of MSST time-frequency images and trained SVM to diagnose bearing faults. e parameter sparsity was set to 0.7; the parameter rank was set to 25 and 100 in datasets CWRU and MFPT, respectively.…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

“…By contrast, the proposed algorithm only consumes less than 100 milliseconds, saving more than 300 times of time, which can better meet the real-time requirements in practical engineering applications. At the same time, the feature coding algorithm in reference [27] is also very time-consuming, which further indicates that the proposed algorithm has high timeliness.…”

Section: Time For Feature Extractionmentioning

confidence: 99%

“…In this regard, Sun et al [27] proposed a method combining MSST and sparse feature coding based on dictionary learning (SFC-DL). Li et al [28] designed a symplectic weighted sparse SMM (SWSSMM) model with the sparsity constraint and low-rank constraint, and Li et al [29] developed the discriminative manifold random vector functional link neural network (DMRVFLNN) model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

Sun

et al. 2021

Shock and Vibration

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Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. Then, the local binary pattern (LBP) operator is introduced to extract the low-dimensional texture features of time-frequency images, which improves the speed of fault recognition. Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively. The feature coding is input into the classifier for training and recognition. Experiments show that our method performs well on the rolling bearing dataset of Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). Further, the proposed method is applied to the loudspeaker pure-tone detection dataset, and the loudspeaker anomaly diagnosis is achieved. The diagnosis results verify that our method can meet the needs of practical engineering.

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Section: Comparisons With Other Methodsmentioning

confidence: 99%

Section: Time For Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

Sun

et al. 2021

Shock and Vibration

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“…The comparison results are shown in Table V. Sun et al [34] used MSST to obtain time-frequency images, and then selected features by non-negative matrix factorization with sparseness constraints. Finally, linear support vector machine was applied to realize fault classification on the rolling bearings.…”

Section: E Compared With State-of-the-art Fault Diagnosis Methodsmentioning

confidence: 99%

Data-Driven Fault Diagnosis Method Based on Second-Order Time-Reassigned Multisynchrosqueezing Transform and Evenly Mini-Batch Training

Sun

Gao

et al. 2020

IEEE Access

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To promote the progress of fault diagnosis system, this study proposes an intelligent and effective fault diagnosis algorithm based on data-driven. Firstly, we propose a new time-frequency analysis method named second-order time-reassigned multisynchrosqueezing transform (STMSST) based on Gaussian-modulated linear group delay (GLGD) model to deal with the vibration signals of fault object for obtaining time-frequency images with high resolution. Then, an improved training method named evenly mini-batch training method is combined with convolutional neural network (CNN) to train and learn fault features from those obtained time-frequency images. Further, the proposed fault diagnosis algorithm is tested on the Case Western Reserve University (CWRU) bearing dataset and the Machinery Failure Prevention Technology (MFPT) Society dataset, respectively, and the experimental results indicate that the feature representation and training effect in our method is superior to state-of-the-art fault diagnosis methods. Finally, the proposed method is applied on the loudspeaker pure-tone detection dataset, which achieves the loudspeaker anomaly diagnosis, and the diagnosis result has verified that the method can meet the needs of practical engineering. INDEX TERMS Evenly mini-batch training, fault diagnosis, second-order time-reassigned multisynchrosqueezing transform.

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“…The method can extract shift-invariant features that have the same distribution for the same health condition, which is crucial for the accurate classification of health conditions. Sun et al [16] applied a sparse coding method to bearing fault diagnosis. The features were extracted from time-frequency images of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

A novel deep neural network based on an unsupervised feature learning method for rotating machinery fault diagnosis

Cheng

Liu

Wang

et al. 2021

Meas. Sci. Technol.

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As a simple and unsupervised feature learning method, sparse filtering has shown potential in rotating machinery fault diagnosis. However, sparse filtering has the following deficiencies: (a) the optimal sparsity of the learned features cannot be determined. (b) As a shallow network, sparse filtering has a limited capability of learning discriminative features under varying loads. (c) The diagnostic accuracy and robustness are insufficient. To overcome these deficiencies, variant sparse filtering (VSF), which can determine the optimal sparsity, is developed. Then, a deep variant sparse filtering network (DVSFN) is constructed by using stacked VSF to enhance the capability of learning discriminative features. Finally, a novel fault diagnosis method using the DVSFN is presented and verified by using rolling bearing and planetary gearbox datasets. The optimal sparsity of the learned features is determined by parametric analysis. The experimental results show that the DVSFN can adaptively learn discriminative features, irrespective of the varying loads, and the developed diagnostic method can achieve higher testing accuracy and stronger robustness in comparison to classic data-driven methods.

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Fine‐Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Cited by 15 publications

References 22 publications

Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

Data-Driven Fault Diagnosis Method Based on Second-Order Time-Reassigned Multisynchrosqueezing Transform and Evenly Mini-Batch Training

A novel deep neural network based on an unsupervised feature learning method for rotating machinery fault diagnosis

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