Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Gao, Jie; Wu, Lifeng; Wang, Hongmin; Guan, Yong

doi:10.3390/app6050154

Cited by 5 publications

(9 citation statements)

References 23 publications

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“…To verify the advancement of the proposed method, this section compares it with the method published in [ 20 ]. The paper used a difference curvature spectrum of incremental singular entropy (DCSISE) to determine the number of effective singular values.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…Previously, the singular value selection depended on experiments or trial and error, which always generated relatively large errors. Some studies have elaborated on this problem [ 20 , 21 , 22 ]. Some researchers tried to seek the singular value by constructing a proper singular spectrum and identifying the turning point.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of this method, however, will be reduced against a strong background of noisy signals, as the method mainly focuses on the maximum peak position of the constructed singular spectrum, which may result in the loss of important information about other peaks. Other researchers proposed selecting an effective singular value based on the asymptotic relationship between singular values and vectors of the signal matrix and the observed matrix [ 20 ]. The filtered signal matrix is reconstructed by minimizing the asymptotic loss.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

Liao

Song

Peng

et al. 2018

Sensors

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Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

Liao

Song

Peng

et al. 2018

Sensors

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“…At the same time, machines have also been more precise than ever before, and machinery fault diagnosis has sufficiently embraced multifault diagnosis revolution in condition monitoring system. Contrasted with top-down modeling proposed by the physics-based fault diagnosis systems, data-driven systems provide a bottomup model to detect the occurrence of machinery faults [3]. As is well-known, the physics-based methods are unable to be updated online with measured data and also cannot deal well with large-scale data.…”

Section: Introductionmentioning

confidence: 99%

A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

Liu

Bao

Han

2018

Mathematical Problems in Engineering

131

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Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function are introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.

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“…However, gears and bearing are prone to breakdown. Therefore, an unexpected failure may lead to serious project accidents, large economic losses, and even human casualties [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

2016

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Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

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Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Cited by 5 publications

References 23 publications

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

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