A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

Jiang, Qinyu; Chang, Faliang

doi:10.1007/s12206-019-0305-2

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…The next step is the classification of feature vectors. The authors used the NN classifier [27], [28], [29], LDA [30], [31], and SVM [25], [32], [33]. However, other classifiers could be also used, for example neural network [34], [35].…”

Section: A Msaf-12mentioning

confidence: 99%

“…The Linear Support Vector Machine (LSVM) is a linear model for classification of data. It is described in the literature [25], [32], [33]. The classifier is useful for recognizing: images, measured data, and text.…”

Section: E Lsvm Classifiermentioning

confidence: 99%

“…The LSVM computes the maximum distance between the two analyzed classes. It was necessary for proper classification of feature vectors [25], [32], [33].…”

Section: E Lsvm Classifiermentioning

confidence: 99%

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Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Głowacz

Kozik

et al. 2019

Measurement Science Review

109

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Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

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Section: A Msaf-12mentioning

confidence: 99%

Section: E Lsvm Classifiermentioning

confidence: 99%

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Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Głowacz

Kozik

et al. 2019

Measurement Science Review

109

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“…In terms of feature classification, neural networks [ 30 , 31 ] and SVM [ 32 , 33 ] have been widely applied in machinery diagnosis. Han et al compared the performance of random forest, artificial neural networks and SVM methods in the intelligent diagnosis of rotating equipment [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery

Wang

Zhao

Zhang

et al. 2021

Sensors

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Feature selection is to obtain effective features from data, also known as feature engineering. Traditional feature selection and predictive model learning are separated, and there is a problem of inconsistency of criteria. This paper presents an end-to-end feature selection and diagnosis method that organically unifies feature expression learning and machine prediction learning into one model. The algorithm first combines the prediction model to calculate the mean impact value (MIVs) of the feature and realizes primary feature selection for the prediction model by selecting the feature with a larger MIV. In order to take into account the performance of the feature itself, the within-class and between-class discriminant analysis (WBDA) method is proposed, and combined with the feature diversity strategy, the feature-oriented secondary selection is realized. Eventually, feature vectors obtained by two selections are classified using a multi-class support vector machine (SVM). Compared with the modified network variable selection algorithm (MIVs), the principal component analysis dimensionality reduction algorithm (PCA), variable selection based on compensative distance evaluation technology (CDET), and other algorithms, the proposed method MIVs-WBDA exhibits excellent classification accuracy owing to the fusion of feature selection and predictive model learning. According to the results of classification accuracy testing after dimensionality reduction on rotating machinery status, the MIVs-WBDA method has a 3% classification accuracy improvement under the low-dimensional feature set. The typical running time of this classification learning algorithm is less than 10 s, while using deep learning, its running time will be more than a few hours.

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“…Furthermore, failure detection methods based on higher-order statistical analysis have been applied to vibration signals, by considering the information provided by cumulants and moments of orders greater than or equal to two, as well as the spatial information that extracted from the analysis of higher-order spectra such as the bispectrum [29,30,33,34,50,63,66]. In addition to statistical estimators such as variance, kurtosis and skewness, derived from the cumulants of second, third, and fourth orders respectively are performed.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors

Martínez

Antonino-Daviu

Córdoba

et al. 2020

Applied Mathematics and Nonlinear Sciences

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AbstractThis work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.

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A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

Cited by 12 publications

References 21 publications

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery

Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors

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