Fault diagnosis method of rolling bearing using principal component analysis and support vector machine

Gu, Yujiong; Zhou, Xueguang; Yu, Dongping; Shen, Yanjun

doi:10.1007/s12206-018-1004-0

Cited by 64 publications

(27 citation statements)

References 24 publications

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“…Content may change prior to final publication. [31] classifier using the radial basis function is selected. The fault characteristics used by the SVM algorithm are the effective value, skewness, kurtosis, peak value, form factor, impulse factor, crest factor, and margin.…”

Section: ) Different Degree Of Failurementioning

confidence: 99%

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

2021

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Section: ) Different Degree Of Failurementioning

confidence: 99%

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

2021

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“…Subsequently, Li and Xue 89 proposed a diagnostic method based on the combination of motor current spectrum analysis (MCSA) and SVM, which successfully extracted the fault characteristics of the stator current, and further improved the accuracy of fault diagnosis and classification. Chen et al 90 combined principal component analysis (PCA) and SVM in order to study the fault characteristics of rolling bearings. The fault feature dimension was effectively reduced, and the accuracy of fault diagnosis after dimension reduction was still as high as 97%.…”

Section: Intelligent Fault Diagnosismentioning

confidence: 99%

Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

Xue

Zhang

et al. 2014

Advances in Mechanical Engineering

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Permanent magnet synchronous motors are the main power output components of electric vehicles. Once a failure occurs, it will affect the vehicle’s power, stability, and safety. While as a complex field-circuit coupling system composed of machine-electric-magnetic-thermal, the permanent magnet synchronous motor of electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency, and communication characteristics make it difficult to diagnose faults. Based on the research of a list of related references, this article reviews the methods of intelligent fault diagnosis for electric vehicle permanent magnet synchronous motors. The research status and development trend of fault diagnosis are analyzed. It provides theoretical basis for motor fault diagnosis and health management in multi-variable working conditions and multi-physics environment.

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“…Another feature learning is feature extraction, which is to project the original feature in a new space to obtain a new feature representation, such as principal component analysis (PCA) and auto-encoder. In existing feature selection or feature extraction algorithms, PCA transforms the original data into linearity-independent data via linear transformation, and it can be used to extract the main feature components of the data [ 22 ]. PCA expands features in the direction in which the covariance is the largest so that the obtained low dimensional features have no corresponding physical meaning.…”

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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Fault diagnosis method of rolling bearing using principal component analysis and support vector machine

Cited by 64 publications

References 24 publications

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

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

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