An adaptive variational mode decomposition based on sailfish optimization algorithm and Gini index for fault identification in rolling bearings

Nassef, M. G. A.; Hussein, Taha M.; Mokhiamar, Ossama

doi:10.1016/j.measurement.2020.108514

Cited by 52 publications

(25 citation statements)

References 50 publications

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“…When VMD analyzes and processes the fault signal, the error is caused by the influence of external factors on both ends [23][24][25][26][27]. Taking into account the end effect in VMD, a mirror extension is adopted to suppress it.…”

Section: The Proposed Diagnosis Methodsmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Shen

Wang

et al. 2021

Mathematical Problems in Engineering

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Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.

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Section: The Proposed Diagnosis Methodsmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Shen

Wang

et al. 2021

Mathematical Problems in Engineering

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“…In the multilevel thresholding procedure, it can be needed to define the optimum threshold value T that maximize the objective function f (T) and is performed by the SFO algorithm. SFO [17] is a population based meta-heuristic technique that was simulated from the attackalternation approach of set of hunting sailfish that hunt a school of sardines. This hunting approach provides upper hand for hunters by giving them the chance of soring their energy.…”

Section: Sfo-te Based Segmentationmentioning

confidence: 99%

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Anupama¹,

Jose²,

Eid³

et al. 2022

Computers, Materials &Amp; Continua

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Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model on Internet of Things (IoT) environment. The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition. In addition, the pre-processing of pap smear images takes place using adaptive weighted mean filtering (AWMF) technique. Moreover, sailfish optimizer with Tsallis entropy (SFO-TE) approach has been implemented for the segmentation of pap smear images. Furthermore, a deep learning based Residual Network (ResNet50) method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images. In order to showcase the improved diagnostic outcome of the BPSIC-CDF technique, a comprehensive set of simulations take place on Herlev database. The experimental results highlighted the betterment of the BPSIC-CDF technique over the recent state of art techniques interms of different performance measures.

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“…Hence, the researchers have paid great attention to the optimization of the two parameters. Numerous intelligent optimization methods, such as particle swarm optimization (PSO), 23 grasshopper optimization algorithm (GOA), 24 and sailfish optimization (SFO), 25 were employed for the automatic parameter selection. For these optimized VMD methods, the signal evaluation indexes are required for the convergence judgment of the optimization process.…”

Section: Introductionmentioning

confidence: 99%

An optimized variational mode extraction method for rolling bearing fault diagnosis

Pang

Nazari

Sun

et al. 2021

Structural Health Monitoring

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The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

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An adaptive variational mode decomposition based on sailfish optimization algorithm and Gini index for fault identification in rolling bearings

Cited by 52 publications

References 50 publications

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

An optimized variational mode extraction method for rolling bearing fault diagnosis

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