Rolling bearing fault diagnosis utilizing variational mode decomposition based fractal dimension estimation method

Yunqiang, Zhang; Ren, Guoquan; Wu, Daming; Wang, Huaiguang

doi:10.1016/j.measurement.2021.109614

Cited by 48 publications

(16 citation statements)

References 31 publications

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“…Concerning that different signal complexity evaluation indexes describe the complexity of the signal from different perspectives as a single index may be incomplete in fault extraction resulting in incorrect fault identification, the paper starts with the similarity and regularity of the signal to describe the complexity of signal. The thinking diagram of paper as is shown in Figure 1 (Chen et al 2021a;Zhang et al, 2021b) and the decomposition layernumber is 4 (Chen et al 2021a;Zhang et al, 2021b).…”

Section: Proposed Novel Methodsmentioning

confidence: 99%

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Identification of rotor–stator rub-impact faults based on dual complexity parameters

Guo

Yunhong

2022

Journal of Vibration and Control

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For effective identification of rub-impact faults between rotor and stator of equipment, the paper has contributed the method integrating dual complexity parameters and variational mode decomposition (VMD). Firstly, to effectively separate the fault characteristics involved in signals, VMD algorithm was applied to decompose vibration signals and the component signals were obtained; secondly, taking account of the large difference of fault feature information involved in different component signals and in order to make options of sensitive fault component signals and reduce the loss of fault features, multi-scale Lempel–Ziv complexity and complexity parameter in Hjorth parameters were brought. Starting from 2 different perspectives of complexity evaluation, choose from the sensitive component signals containing more fault characteristics with these 2 complexity parameters; thirdly, signals were reconstructed based on selected sensitive component signals, and meanwhile, singular value difference spectrum algorithm was used to denoise reconstructed signals to further lessen the influence of noises; finally, the rub-impact fault between rotor and stator was identified by square demodulation spectrum (SDS) of denoised signal. The effectiveness of the proposed method has been proved by comparative analysis with other approaches as well as validation analysis of rub-impact fault signals in multiple situations.

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Section: Proposed Novel Methodsmentioning

confidence: 99%

“…(1) Vibration signals are decomposed into component signals by VMD. Penalty factor in VMD is 2000 (Chen et al 2021a; Zhang et al, 2021b) and the decomposition layer-number is 4 (Chen et al 2021a; Zhang et al, 2021b).…”

Section: Proposed Novel Methodsmentioning

confidence: 99%

Identification of rotor–stator rub-impact faults based on dual complexity parameters

Guo

Yunhong

2022

Journal of Vibration and Control

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“…e results show that the improved VMD is helpful to improve the accuracy of signal decomposition. Zhang et al [5] decomposed the signal into multiple components using VMD, estimated the fractal dimension using the least square method, and finally carried out fault diagnosis according to the fractal dimension of the two scales. e results show that the two-scale fractal dimension extracted by VMD can express the fractal characteristics of vibration signals and further carry out fault diagnosis.…”

Section: Introductionmentioning

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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“…Fortunately, a completely non-recursive method named as variational mode decomposition (VMD) [18] proposed by Konstantin et al could be used as an alternative time-frequency analysis method for wavelet transform and EMD to decompose the non-stationary and multi-component vibration signal of faulty bearing. The fractal features of the vibration signal of faulty bearing was studied by the proposed novel fractal dimension estimation method based on VMD, and analyzed results indicated that the fractal characteristics of vibration signals could be expressed by the proposed method effectively [19]. VMD was used to extract the weak fault transients for realizing the fault diagnosis of gearbox under varying speed successfully [20].…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing weak fault diagnosis utilizing successive variational mode decomposition with sparsity index reconstructing strategy

Jiang¹,

Wang²,

Du³

2022

J. vibroeng.

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By introducing much stricter criteria into VMD, the successive VMD (SVMD) originating from VMD is proposed, which is more suitable for extracting fault feature of faulty rolling bearing than VMD, especially the inner race failure or cage failure, because the frequency spectrum structure of the above two kinds of failures take on the compact frequency spectrum distribution characteristic of the center frequency with sidebands, which is compliant with the criteria of SVMD. In addition, the optimal number of modes does not need to be determined in advance for SVMD, and SVMD could decompose and extract the optimal modes adaptively. So SVMD is used in the paper for modes extraction of the faulty signal of rolling bearing. Unfortunately, the impulse characteristic components might be distributed in each mode more or less in the decomposition processes of SVMD, and the fault features is impossible to be extracted effectively only based on a single mode. Accordingly, a mode regrouping strategy based on sparsity index is proposed to regroup the modes containing useful fault information. At last, the regrouped signal is analyzed by using envelope spectral and satisfactory fault features are extracted. Through the verification of simulation, SVMD has better comprehensive performance compared with other time-frequency analysis methods such as VMD, VME and EEDD. Effectiveness of the proposed method is verified by one experimental and one engineering vibration data respectively. Besides, the advantage of the proposed method compared with the advanced analysis method, that is, Mkurtogram is also verified through comparison.

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Rolling bearing fault diagnosis utilizing variational mode decomposition based fractal dimension estimation method

Cited by 48 publications

References 31 publications

Identification of rotor–stator rub-impact faults based on dual complexity parameters

Identification of rotor–stator rub-impact faults based on dual complexity parameters

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

Rolling bearing weak fault diagnosis utilizing successive variational mode decomposition with sparsity index reconstructing strategy

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