Singular value decomposition packet and its application to extraction of weak fault feature

Zhao, Xuezhi; Bang-yan, YE

doi:10.1016/j.ymssp.2015.08.033

Cited by 54 publications

(27 citation statements)

References 21 publications

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“…In definition, the SVD is an orthogonal decomposition method, for a matrix A2 R mÂn , there can be two orthogonal matrices: The details of SVD are described mainly as follows. 23,24 Step 1: Embedding.…”

Section: Singular Value Decomposition With Hankel Matrixmentioning

confidence: 99%

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

Tong

Zhang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.

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Section: Singular Value Decomposition With Hankel Matrixmentioning

confidence: 99%

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

Tong

Zhang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…As a data processing method, SVD has been successfully applied to signal denoising processing and proved to be effective in avoiding modal aliasing. The main application of singular value decomposition in statistics is the principal component analysis (PCA), which can be used in pattern recognition [8][9][10][11], data dimensionality reduction [12,13], filter design [14,15], denoising [16,17], feature extraction and weak signal separation [18][19][20][21][22], face recognition [23,24] and many other fields, and it has gained important applications. In10, the application of SVD in pattern recognition of partial discharge signals is studied.…”

Section: Introductionmentioning

confidence: 99%

Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

Zhang

et al. 2020

Applied Sciences

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Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

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“…Inaccurate parameters of a physical model can significantly affect the efficiency of model based early detection of small slowly varying faults. For the purpose of developing a data-driven small fault detection method, many necessary techniques have been used, such as a filter-based method, exponential weighted moving average (EWMA) based method and a cumulative sum (CUSUM) based method [ 23 , 24 , 25 , 26 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…For model based RUL prediction, if there is an exact damage precursor determined by experienced experts and the fault size itself is observable, then a damage precursor based RUL prediction method can be used [ 33 , 34 , 35 , 36 , 37 , 38 ]. However, the accuracy of the precursor greatly affects the prediction error.…”

Section: Introductionmentioning

confidence: 99%

Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

Zhou

Park

Wen

et al. 2018

Sensors

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Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method can increase the fault size as well as decrease the noise energy. Then, designated component analysis (DCA) is introduced for developing an AA-DCA method to diagnose the root cause of the fault, which is helpful for the operator to make maintenance decisions. Combining the advantage of the cumulative sum (CUSUM) based method and the AA based method, a CUSUM-AA based method is developed to detect faults at earlier times. Finally, the remaining useful life (RUL) prediction model with error correction is established by nonlinear fitting. Once online fault size defined by detection statistics is obtained by an early diagnosis algorithm, real-time RUL prediction can be directly estimated without extra recursive regression.

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Singular value decomposition packet and its application to extraction of weak fault feature

Cited by 54 publications

References 21 publications

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

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