A parameter-adaptive variational mode decomposition approach based on weighted fuzzy-distribution entropy for noise source separation

Zhou, Qidi; Zhang, Junhong; Zhou, Tianyi; Qiu, Yongbo; Jia, Hongjie; Lin, Jiewei

doi:10.1088/1361-6501/aba3f3

Cited by 10 publications

(5 citation statements)

References 31 publications

(34 reference statements)

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“…In general, the fault signal of rolling bearings contains three components, namely the transient impulse train d (caused by rolling bearing faults), environmental noise e, and discrete harmonic signal u [30,31]. These three components are affected by system dynamic transmission-path effect h.…”

Section: Deconvolution Problemmentioning

confidence: 99%

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

Fang

Yang

et al. 2021

Meas. Sci. Technol.

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Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

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Section: Deconvolution Problemmentioning

confidence: 99%

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

Fang

Yang

et al. 2021

Meas. Sci. Technol.

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“…In this regard, scholars have adopted the idea of entropy to optimize the parameters of VMD adaptively. Guo et al 18 used the multi-scale permutation entropy (MPE) threshold method to select the appropriate VMD parameters, and obtained the optimal intrinsic mode function (IMF) components of the bearing fault signal; He et al 19 used the improved sparrow search algorithm to optimize VMD parameters with the dispersion entropy as the fitness value, and effective noise reduction of bearing fault signals was achieved; Ren et al 20 used the improved adaptive genetic algorithm (IAGA) to optimize VMD parameters with the permutation entropy as the fitness value, thus realizing the separation of effective information of diesel engine fault signals; considering the complexity of the signal system and the mutual information between the decomposed components and the original signal, Zhou et al 21 constructed a weighted fuzzy distribution entropy (FuzzDistEn) to optimize VMD to adaptively obtain optimal parameters; this method successfully separates five noise sources in the diesel engine vibration signal and improves the SNR of the signal. Inspired by the above research, to realize adaptive noise reduction for different types of fault signals of bearings and diesel engines and retain rich fault information, this paper uses the gray wolf optimization algorithm (GWO) to optimize the two important parameters of Alpha and K in VMD with the power spectrum entropy as the fitness value.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive fault diagnosis algorithm for multi-machine equipment: application in bearing and diesel engine

Liu

Kang

Bai

et al. 2022

Structural Health Monitoring

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This paper proposes an adaptive fault diagnosis algorithm based on vibration signals for fault diagnosis of bearings and diesel engines. First, the improved nonlinear gray wolf optimization algorithm (NGWO) is adopted to optimize the key parameter for variational mode decomposition (VMD) with the power spectral entropy as the fitness value. Meanwhile, adaptive noise reduction of the signal is realized. Then, sensitive fault features of bearings and diesel engines are selected through a feature sensitivity analysis on the vibration signals. Also, a single-layer sparse autoencoder is used to align the feature dimensions of each type of data to construct feature matrix samples. Subsequently, a deep neural network (DNN) consisting of a two-layer stacked sparse autoencoder (SSAE) and a Softmax classification layer is constructed to realize failure mode recognition. During the training process of DNN, a surrogate model formed by NGWO and a back propagation neural network is employed to optimize the hyperparameters of SSAE. Finally, to verify the effectiveness of the proposed fault diagnosis algorithm, fault diagnosis experiments are conducted on the fault data set of bearings and diesel engines. The diagnosis results show that the proposed method achieves high-precision fault diagnosis for bearings and diesel engines and performs stably for small samples.

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“…Dibaj et al [22] determined the optimal values of decomposition parameters K and alpha by judging the adaptive indices, including the mean correlation coefficients between the adjacent modes and the energy loss coefficient between the original signal and the reconstructed signal, which should not exceed the defined thresholds for optimal values. With the rapid development of bionic optimization technology, various metaheuristic algorithms were used to simultaneously optimize the K and α of VMD, such as a genetic algorithm (GA) [23], particle swarm optimization (PSO) [24], a grasshopper optimization algorithm (GOA) [25], and a whale optimization algorithm (WOA) [26]. Although these parameter-adaptive VMD methods have achieved good results in their respective research fields, the inefficiency of the optimization process is an urgent problem.…”

Section: Introductionmentioning

confidence: 99%

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

Xinfeng

Yabing

et al. 2022

Energies

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This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research.

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A parameter-adaptive variational mode decomposition approach based on weighted fuzzy-distribution entropy for noise source separation

Cited by 10 publications

References 31 publications

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

A novel adaptive fault diagnosis algorithm for multi-machine equipment: application in bearing and diesel engine

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

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