Fault diagnosis of train rotating parts based on multi-objective VMD optimization and ensemble learning

Jin, Zhenzhen; He, Deqiang; Ma, Rui; Zou, Xueyan; Chen, Yanjun; Shan, Sheng

doi:10.1016/j.dsp.2021.103312

Cited by 47 publications

(22 citation statements)

References 39 publications

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“…Its decomposition results are influenced by K and , so it is important to optimize the parameters of VMD. Reference [11,15] a gives a specific theory.…”

Section: Variational Modal Decompositionmentioning

confidence: 99%

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Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Jin

Lao

et al. 2022

Nonlinear Dyn

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The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intensive noise. Thus, it is still a great challenge to perform early fault diagnosis. An intelligent early fault diagnosis method for rotating machines is proposed based on Variational Mode Decomposition (VMD) parameter optimization and Deep Multi-Kernel Extreme Learning Machine (DMKELM). First, the iterative chaotic mapping, nonlinear convergence factor and inertia weight are introduced into the whale optimization algorithm (WOA). Then, an improved WOA (IWOA) is presented and utilized to optimize VMD parameters. The optimized VMD (OVMD) combined with the sampling entropy is used to denoise and reconstruct the signals. Finally, the mish function is served as the activation function. Meanwhile, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function. The mixed kernel assignment is applied to replace the random assignment, and the DMKELM is presented to enhance the classification performance and generalization capability of the model, and DMKELM is used for early intelligent fault diagnosis. Two experiments on bearings and gears demonstrated that the fault diagnosis accuracy is 99% and 98.5%, respectively, and the fault diagnosis accuracy is high. It shows that this method has great superiority in early fault diagnosis of rotating machinery.

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“…Its decomposition results are influenced by K and , so it is important to optimize the parameters of VMD. Reference [11,15] a gives a specific theory.…”

Section: Variational Modal Decompositionmentioning

confidence: 99%

“…VMD [14] is an adaptive signal analysis method and it performs better than EMD, EWT and EEMD in signal decomposition and feature extraction [15]. Li [16] used VMD to further separate the weak fault influence components, and accurately realized fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Jin

Lao

et al. 2022

Nonlinear Dyn

Self Cite

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“…e VMD algorithm is a vibration signal processing method that decomposes complex signals into a series of AM-FM signals around a fixed center frequency and limited bandwidth [12].…”

Section: Vmd Algorithmmentioning

confidence: 99%

“…When R 2 ≥ ST, the sparrow group needs to move to a safe area immediately to avoid intruders [18]. e predator updates the position according to formula (11), and the sparrow with alert ability updates the position according to formula (12).…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Intelligent Fault Severity Detection of Rotating Machines Based on VMD-WVD and Parameter-Optimized DBN

Jia

Cheng

Tian

et al. 2022

Shock and Vibration

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An intelligent fault severity detection method based on variational mode decomposition- (VMD-) Wigner-Ville distribution (WVD) and sparrow search algorithm- (SSA-) optimized deep belief network (DBN) is suggested to address the problem that typical fault diagnostic algorithms are inappropriate for extremely comparable vibration signals when the samples are insufficient. VMD is used to process the original vibration signal to obtain the band intrinsic mode functions (BIMFs) with different frequencies. WVD produces the two-dimensional spectrum of the key BIMF with the highest variance contribution rate. The input sample of DBN is composed of a characteristic matrix formed by the two-dimensional spectrum of multiple fault signals. DBN’s learning rate and batch size are both tuned globally by SSA, which has a significant influence on network error. The fitness function in the parameter optimization process is the network’s root mean square error (RMSE). Finally, the input samples are loaded into a DBN that has the best structure for detecting severity. Experiments show that, based on VMD-WVD and SSA-DBN, accuracy of the fault severity detection model for rotating machines, which has good generalization ability and robustness, can reach 98%. Compared with BPNN, the traditional DBN, VMD-DBN, VMD-PSO-DBN, and other methods, the proposed algorithm has strong adaptive feature extraction ability and generalization of application.

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“…Their results showed high diagnostic performance and adaptability on the Case Western Reserve University Bearing dataset. The influence of track excitation and other equipment’s vibration which may result in the presence of non-gaussian noise in the vibration signal was tackled by Jin et al [ 35 ] through the use of multi-objective VMD optimization and ensemble learning for rotating machine diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning

Inyang

Petrunin

Jennions

2023

Sensors

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Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

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Fault diagnosis of train rotating parts based on multi-objective VMD optimization and ensemble learning

Cited by 47 publications

References 39 publications

Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Intelligent Fault Severity Detection of Rotating Machines Based on VMD-WVD and Parameter-Optimized DBN

Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning

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