A novel intelligent approach based on WOAGWO-VMD and MPA-LSSVM for diagnosis of bearing faults

Taïbi, Ahmed; Ikhlef, N.

doi:10.1007/s00170-022-08852-7

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…Zhou et al [200] combined WOA with gray wolf optimization (GWO) to form whale GWO algorithm (WGWOA), and used SVM optimized by WGWOA to enhance the accuracy and adaptability of model classification. Taibi et al [201] adopted marine predators algorithm to optimize the key parameters of LSSVM, and verified the validity and strength of the proposed method through experimental data under different working conditions.…”

Section: Fault Feature Identification Based On Intelligent Optimizati...mentioning

confidence: 92%

“…The classical machine learning algorithms: BP, DT, SVM and LSSVM, the intelligent optimization algorithms improved Y NL and NS [189] Y Small-sample, NL [190] Y NL and NS, impulsive noise [191] Y NL and NS [192] -Big data, quantum Intelligent optimization algorithms-SVM [194] Improve SVM parameters Slow optimization speed, many adjustment parameters -Mixed noise [195] Y Complex imbalanced data [196] Y NL and NS [197] -Multi-channel signals [199] Y EFS [200] Y NL and NS [201] Y SVM and the deep learning algorithms: CNN and LSTM are compared from the key features, application difficulties and application occasions (table 3). By combing and comparing the common methods of fault feature identification, the classical machine learning algorithms and deep learning algorithms can achieve better application results in different occasions.…”

Section: Comparative Analysis Of Fault Feature Identification Methodsmentioning

confidence: 99%

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Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

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Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University (CWRU) bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.

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Section: Fault Feature Identification Based On Intelligent Optimizati...mentioning

confidence: 92%

Section: Comparative Analysis Of Fault Feature Identification Methodsmentioning

confidence: 99%

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

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“…Rolling bearings are one of the most commonly used and vulnerable basic components in induction machines, and their failure is one of the most common causes of machine failures. According to many researches, more than 44% of induction machinery equipment failures are due to bearing faults (Taibi et al, 2022). It is, therefore, necessary to instantaneously and accurately detect and diagnose faults of rolling bearings to avoid catastrophic malfunctions and increase system reliability.…”

Section: Introductionmentioning

confidence: 99%

“…To extract effective fault feature information from the vibrational signal data, which has been processed and denoised using the VMD_DWT algorithm, various entropy-based feature extraction techniques are applied, which are widely used in the field of fault diagnosis, such as sample entropy (SE) (Yang, 2012), permutation entropy (PE) (Taibi et al, 2022;Noman et al, 2021) and multiscale entropy (Li et al, 2018). Nonetheless the above methods have some inherent flaws, for example, in SE.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Taibi,

Touati,

Aomar

et al. 2024

COMPEL

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Purpose Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine. Design/methodology/approach To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification. Findings The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals. Originality/value This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

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“…Effectively separating the core resonance frequency band and eliminating the shrinkage difference of sidebands at different speeds is an effective way to achieve bearing fault diagnosis at multiple speeds. Variational modal decomposition (VMD) is a noniterative signal adaptive decomposition method, which can effectively extract the main resonance bands in the signal spectrum through center frequency search and sideband reconstruction (Li et al, 2018; Taibi et al, 2022; Xu et al, 2021). Compared with the traditional empirical mode decomposition (EMD) and local mean decomposition (LMD) methods, better signal decomposition effect can be obtained (Hoseinzadeh et al, 2019; Li et al, 2022c; Zuo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Research on bearing fault diagnosis method based on SCVMD and CGLF under various rotating speeds

2023

Transactions of the Institute of Measurement and Control

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To solve the problem of bearing fault diagnosis at different speeds, a fault diagnosis method based on single-component variational modal decomposition (SCVMD) and coarse-grained lattice feature (CGLF) is proposed by analyzing the influence mode of speed transformation on frequency spectrum. First, the central frequency of the main resonance band of the signal is extracted based on SCVMD to eliminate the problem of spectrum shift caused by speed change. Then, the signal fragments are intercepted from the original signal spectrum to construct CGLF. Finally, a deep convolutional neural network (DCNN) is established to solve the sideband shrinkage problem caused by speed change and used to construct the mapping relationship between CGLF and category labels. In the experiment, bearing fault experimental platform dataset is used for the algorithm verification, and the final recognition rate is 98.3%. It proves that the method can effectively achieve bearing fault diagnosis at different speeds.

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A novel intelligent approach based on WOAGWO-VMD and MPA-LSSVM for diagnosis of bearing faults

Cited by 8 publications

References 51 publications

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Research on bearing fault diagnosis method based on SCVMD and CGLF under various rotating speeds

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