Self-Adaptive Multivariate Variational Mode Decomposition and Its Application for Bearing Fault Diagnosis

Song, Qiuyu; Jiang, Xingxing; Wang, Shuang; Guo, Jianfeng; Huang, Weiguo; Zhu, Zhongkui

doi:10.1109/tim.2021.3139660

Cited by 24 publications

(21 citation statements)

References 33 publications

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“…For comparison, two classic multichannel signal processing methods (MEMD and MVMD [27]) are employed in this case. The result of MEMD is shown in Fig.…”

Section: A Bearing Inner-race Fault Diagnosismentioning

confidence: 99%

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Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Zhang

Yuan

et al. 2023

IEEE Sensors J.

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In practical engineering applications, the multivariate signal contains more fault feature information than the single channel signal. How to realize synchronous extraction of fault features from the multivariate signal is of great significance in fault diagnosis of rotary machinery. Dynamic mode decomposition (DMD) has attracted much attention due to its excellent dynamic feature extraction ability. However, DMD lacks mode aliasing property when dealing with the multivariate signal, which may lead to the loss of critical fault feature information. Cater to this problem, this paper proposed a multivariate dynamic mode decomposition (MDMD) algorithm which is the multivariate extension of DMD. First, snapshot tensors are defined to convert multivariate signals to tensor format. Then, MDMD algorithm is proposed by introducing tensor operations into the original DMD algorithm, where a tensor low tubal rank component extraction framework is constructed to enable simultaneous extraction of bearing fault features from the multivariate signal, to enhance the robustness and effectiveness of the algorithm. Finally, both numerical simulations and experiments verify the proposed MDMD has higher fault diagnosis accuracy than other multivariate signal processing methods.

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“…For comparison, two classic multichannel signal processing methods (MEMD and MVMD [27]) are employed in this case. The result of MEMD is shown in Fig.…”

Section: A Bearing Inner-race Fault Diagnosismentioning

confidence: 99%

“…However, the performance of MVMD depends on the selection of two key parameters. Song et al [27] proposed a self-adaptive MVMD method for bearing fault diagnosis. They determined the mode parameters and initial center frequencies based on the convergence trend of MVMD.…”

mentioning

confidence: 99%

Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Zhang

Yuan

et al. 2023

IEEE Sensors J.

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“…Dragomiretskiy and Zosso (2014) proposed a completely nonrecursive signal decomposition method, the VMD algorithm. Compared with signal time-frequency domain analysis methods such as EMD, ensemble empirical mode decomposition (EEMD), and wavelet decomposition, VMD breaks away from the common thinking pattern of progressive recursive decomposition and transforms it into solving constrained variational problems, which can better analyze unbalanced signals and solve modal aliasing and adaptive problems (Liu and Tan, 2022;Miao et al, 2022;Song et al, 2022;Zhang et al, 2022).…”

Section: Mathematical Theory Variational Mode Decompositionmentioning

confidence: 99%

“…Their safe and reliable operation state determines the safety and economic benefits of using mechanical equipment. Among all types of faults related to rotating machinery, the most obvious is the abnormal vibration of a rotor system (Liu and Tan, 2022;Miao et al, 2022;Song et al, 2022;Zhang et al, 2022). Therefore, it is important to improve the safe and efficient operation of mechanical equipment to fully explore the relationship between the rotor vibration signal and rotor system operation state.…”

Section: Introductionmentioning

confidence: 99%

Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM

Wang

Liu²,

Liu

et al. 2022

Front. Energy Res.

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The rotor system is a core part of rotating machinery equipment. Its safe and reliable operation directly affects the economic benefit of using the equipment and the personal safety of users. To fully explore the complex feature mapping relationship between rotor vibration signals and fault types, rotor vibration signals were studied under different working conditions from the perspective of feature parameter construction and feature information mining. First, a variational mode decomposition algorithm was used to decompose the vibration signals, and quantum behavior particle swarm optimization was used to minimize the mean envelope entropy of intrinsic mode function components to determine the optimal combination of modal number and penalty coefficient. Second, the principal component analysis was used to reduce the dimensionality of IMF components of vibration signals. Finally, a support vector machine was used to mine the feature mapping relationship between vibration data after dimensionality reduction and rotor operation state to accurately identify rotor fault types. The proposed method was used to analyze the measured vibration signals of the rotor system. The experimental results showed that the proposed method effectively extracted characteristic information of the rotor running state from the vibration data, and the accuracies of four types of fault diagnoses were 100%, 88.89%, 100%, and 100%, respectively. In addition, the accuracies of the four fault diagnoses in this study were better than those of the previously reported models.

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“…Wang et al [24] developed the complex variational mode decomposition (CVMD) method to deal with the complex-valued signals. Song et al [25] developed the self-adaptive multivariate variational mode decomposition (MVMD) for multichannel bearing vibration signals decomposition. In this work, signals monitored by multiple sensors are co-decomposed by the multivariate variational mode decomposi-tion (MVMD) method to obtain signal components at different scales.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

et al. 2022

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Deep learning based on vibration signal image representation has proven to be effective for the intelligent fault diagnosis of bearings. However, previous studies have focused primarily on dealing with single-channel vibration signal processing, which cannot guarantee the integrity of fault feature information. To obtain more abundant fault feature information, this paper proposes a multivariate vibration data image representation method, named the multivariate symmetrized dot pattern (M-SDP), by combining multivariate variational mode decomposition (MVMD) with symmetrized dot pattern (SDP). In M-SDP, the vibration signals of multiple sensors are simultaneously decomposed by MVMD to obtain the dominant subcomponents with physical meanings. Subsequently, the dominant subcomponents are mapped to different angles of the SDP image to generate the M-SDP image. Finally, the parameters of M-SDP are automatically determined based on the normalized cross-correlation coefficient (NCC) to maximize the difference between different bearing states. Moreover, to improve the diagnosis accuracy and model generalization performance, this paper introduces the local-to-global (LG) attention block and locally enhanced positional encoding (LePE) mechanism into a Swin Transformer to propose the LEG Transformer method. Then, a novel intelligent bearing fault diagnosis method based on M-SDP and the LEG Transformer is developed. The proposed method is validated with two experimental datasets and compared with some other methods. The experimental results indicate that the M-SDP method has improved diagnostic accuracy and stability compared with the original SDP, and the proposed LEG Transformer outperforms the typical Swin Transformer in recognition rate and convergence speed.

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Self-Adaptive Multivariate Variational Mode Decomposition and Its Application for Bearing Fault Diagnosis

Cited by 24 publications

References 33 publications

Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

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