Bearing Fault Feature Extraction Based on Adaptive OMP and Improved K-SVD

Wang, Lijun; Li, Xiangyang; Xu, Da; Ai, Shijuan; Wang, Chaoge; Chen, Changxin

doi:10.3390/pr10040675

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…The harmonic signal can be separated quickly and efficiently by using adaptive orthogonal matching pursuit algorithm. We performed a comparative analysis of different algorithms on signals with specific harmonics and noise added and evaluated the results as Table 1 [34]. The adapOMP has the strongest harmonic extraction ability and less time when it is used alone.…”

Section: Improved Sparse Representation Algorithmmentioning

confidence: 99%

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Wang

et al. 2022

Processes

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Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.

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Section: Improved Sparse Representation Algorithmmentioning

confidence: 99%

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Wang

et al. 2022

Processes

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“…Wu et al [23] constructed dictionaries using different fabric samples and successfully reconstructed the samples using K-SVD, thus confirming the effectiveness of the algorithm. Wang et al [24] proposed a method that combines the orthogonal matching pursuit (OMP) algorithm with K-SVD, illustrating the feasibility of K-SVD for noise reduction in signals from rotating machinery, such as rolling bearings. Based on the research conducted by the aforementioned scholars, K-SVD has been recognized as an effective method for noise reduction and signal decomposition.…”

Section: Introductionmentioning

confidence: 99%

Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study

Mao,

Zeng,

Tan

et al. 2023

Sensors

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To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, α, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals.

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“…Thus, KSVD is commonly used to extract fault features of rolling bearing vibration signals. [23][24][25][26] Recently, other DL methods are also developed based on KSVD. For instance, Zhang and Li and Li 27 proposed a discriminative model for KSVD algorithm to enhance its discriminative level by incorporating the classification error into the objective function.…”

Section: Introductionmentioning

confidence: 99%

An adaptive kernel dictionary learning method based on grey wolf optimizer for bearing intelligent fault diagnosis

Yang

Zhang

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with the capacity to handle nonlinear signals. However, the choice of kernel parameters and sparse level is a key issue, since these parameters respectively determine the form of the high-dimensional kernel space and the capability of KKSVD to learn appropriate atomic information for representing the samples. As a result, it is difficult to achieve the maximum performance of KKSVD by pre-specifying the values of the parameters. To address this issue, an advanced meta-heuristic algorithm – that is, the grey wolf optimizer (GWO) is introduced into the KKSVD. Specifically, an objective function is first designed, in which the parameters to be optimized are involved in the learning process of KKSVD for the bearing train set and then applied to the testing of the bearing validation set to get the classification results. The classification accuracy is fed back to the GWO algorithm which will update the parameters iteratively and output the optimal parameters. Two case studies respectively corresponding to two common situations in bearing fault diagnosis – that is, strong noisy samples and unbalanced samples, are carried out. The analysis results demonstrate the effectiveness of the proposed method for adaptively obtaining the optimal parameters and improving the performance of KKSVD. Furthermore, the proposed method outperforms several state-of-art dictionary methods in terms of diagnosis accuracy and robustness.

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Bearing Fault Feature Extraction Based on Adaptive OMP and Improved K-SVD

Cited by 6 publications

References 31 publications

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study

An adaptive kernel dictionary learning method based on grey wolf optimizer for bearing intelligent fault diagnosis

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