Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Zhang, Qixiang; Yuan, Rui; Lv, Yong; Li, Zhaolun; Wu, Hongan

doi:10.1109/jsen.2023.3248285

Cited by 26 publications

(9 citation statements)

References 38 publications

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“…The failure of rolling bearings can cause issues in mechanical equipment operations. Achieving rapid and precise fault diagnosis is of significant practical relevance to ensure the uninterrupted operation of the equipment [32,33]. Traditional DL-based fault diagnosis method has the drawbacks of weak feature information of fault samples, high model complexity, and low diagnosis accuracy.…”

Section: The Proposed Novel Fault Diagnosis Approach Of Rolling Beari...mentioning

confidence: 99%

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Yuan

et al. 2023

Meas. Sci. Technol.

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Rolling bearings play a crucial role as components in mechanical equipment. Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose safety hazards. Traditional deep learning (DL)-based methods for diagnosing faults in rolling bearings present several issues, such as insufficient feature information of fault samples, high model complexity and low accuracy. To overcome these challenges, this paper introduces an intelligent approach for rolling bearing fault diagnosis using intrinsic feature extraction (IFE) and convolutional block attention module (CBAM)-enhanced InceptionNet. In our researches, variational mode decomposition (VMD) is adopted to decompose the original signal into multiple band-limited intrinsic mode functions (BLIMFs). In the decomposition process of VMD, the number of decomposition layers k is determined by center frequency method and the optimal BLIMF is chosen based on minimum envelope entropy. Subsequently, the continuous wavelet transform (CWT) is employed to transform the optimal BLIMF into time-frequency images. Finally, the obtained time-frequency images are fed into the proposed CBAM-enhanced InceptionNet for fault state diagnosis. Experiments on two different datasets prove that the method has stable and reliable accuracy. Comparative experiments have demonstrated that this method can reduce network model parameters and improve diagnosis efficiency while achieving high accuracy.

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Section: The Proposed Novel Fault Diagnosis Approach Of Rolling Beari...mentioning

confidence: 99%

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Yuan

et al. 2023

Meas. Sci. Technol.

Self Cite

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“…In recent years, there has been notable development in nonlinear dynamics, and related methods such as wavelet transform (WT) [6], empirical mode decomposition (EMD) [7], and variational mode decomposition (VMD) [8] have been extensively utilized in bearing fault diagnosis [9][10][11][12][13][14]. Although WT is effective in extracting bearing fault features from vibration signal, it heavily relies on human engineering experience for choosing wavelet basis functions, making it less adaptable [15]. On the other hand, EMD offers a flexible approach to process nonlinear and non-stationary signals, eliminating the requirement for manual parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Improved multiscale coded dispersion entropy: a novel quadratic-coded health indicator of rolling bearings

Fan,

Yuan,

et al. 2024

Meas. Sci. Technol.

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Dispersion entropy (DE) is widely used to quantify the complexity of nonlinear time series. In order to improve the ability to capture fault characteristics, a novel approach called coded dispersion entropy (CDE) has been introduced in recent years. CDE aims to expand the number of possible dispersion patterns and enhance the encoding of similar dispersion patterns. However, the coding method of CDE ignores the amplitude differences between dispersion elements and average elements, resulting in the inaccurate assignment of samples. Additionally, CDE is unable to extract effective information from other time scales. These limitations hinder the ability of CDE to effectively characterize faults. This paper proposes an improved multiscale coded dispersion entropy (IMCDE) to solve these limitations. The method introduces the interval scaling factor "R" and utilizes the sum and difference between the mean element and R as new coding boundaries. This approach addresses the sensitivity issue of the CDE coding mode towards smaller amplitude differences by the reasonable quadratic coding mode. Additionally, a composite coarse-graining process is introduced to rearrange the first coarse-grained point in sequence, resulting in multiple sets of sequences. The average probability of the same dispersion pattern at each scale is calculated to correct the entropy error. The experimental results from two sets of bearing faults demonstrate the effectiveness of this method in extracting critical features associated with faulty bearings. Furthermore, the method showed higher classification accuracy compared to multiscale dispersion entropy (MDE) and multiscale coded dispersion entropy (MCDE). Additionally, it exhibited smaller classification error than MDE and MCDE.

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“…Yuan et al [14] proposed a multivariate intrinsic multiscale entropy (MIME) analysis based on multivariate variational mode decomposition (MVMD). Zhang et al [15] proposed a multidimensional dynamic mode decomposition (MDMD), which solves the problem of mixed-mode characteristics and possible loss of critical fault feature information when processing multivariate signals.…”

Section: Introductionmentioning

confidence: 99%

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

Gao,

Sun,

et al. 2024

International Journal of Energy Research

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The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

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Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

Cited by 26 publications

References 38 publications

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Improved multiscale coded dispersion entropy: a novel quadratic-coded health indicator of rolling bearings

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

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