A Method for Identifying Weak Faults of Rolling Bearings Around Railway Based on PCA&amp;SVD-LMD

Liu, Han; Qi, Xiangyi; Xu, Kang; Xiao, Xiaowu

doi:10.23919/chicc.2019.8866271

Cited by 1 publication

(2 citation statements)

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“…According to Algorithm 3, 60 groups are randomly selected from the state category of each bearing as the training set, and 60 groups are used as the test set, which are labeled according to the state category that they belong to. To illustrate the effectiveness of the proposed method, PCA, SVD, and WSVD [ 30 ] are selected as the comparison, Furthermore, the SVM classifier is obtained from the training set data, and the classification accuracy and diagnosis time of the test set data by the SVM classifier are used as the criteria for assessing the optimal diagnosis method. To further illustrate the effectiveness of MWSVD method that is proposed in this paper, after feature extraction three feature extraction methods are visualized and analyzed to observe the effects of feature extraction.…”

Section: Experiments and Analysis Resultsmentioning

confidence: 99%

“…When compared with PCA, the singular value has good stability and it is not sensitive to changes that are caused by interference, such as noise. It can still collect data information more accurately, even with small interference [ 29 , 30 ]. Kedadouche et al [ 31 ] applied SVD to extract the matrix after WPT and use it as the input of SVM to identify the fault mode of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Zhu

Wei

et al. 2021

Sensors

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Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

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Section: Experiments and Analysis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%