Research on a new method of diagnosis of bearing fault diagnosis based on CMFAE and IALMMO-0
LIU Dongyao
Abstract：In order to reduce external interference and improve the accuracy of bearing fault diagnosis,this article proposes a new method based on Composite Multiscale Fractional-order Attention Entropy(CMFAE)and Improving Autonomous Learning Multi-model 0-Order(IALMMo-0).First of all,in response to the defects of entropy,this article proposes a Composite Multiscale Fractional-order Attention Entropy(CMFAE),And the original vibration signal is comprehensively extracted using CMFAE.Secondly,in order to avoid more redundant information in the sample,which affects the accuracy rate of fault diagnosis,the Linear Discriminant Analysis(LDA)is used to reduce the dimension of the obtained feature vector.Finally,for the defects of the Autonomous Learning Multi-model 0-Order(ALMMo-0)Classifier,this article proposes to use the information entropy weight method to weight the Mahalanobis distance,and then use Pearson correlation coefficient to improve the weighted Mahalanobis distance,forming Improved Weighted Mahalanobis Distance(IWMD);And IWMD was used to improve its classifier,forming IALMMo-0 classifier;Meanwhile,train the IALMMo-0 classifier using feature vectors and test its performance.In order to test the accuracy and effectiveness of the new methods mentioned in the article,the bearing data was used to analyze in the test,and the accuracy of the failure recognition was as high as 95.601%through test analysis.
Keywords：bearingcomposite multiscale fractional-order attention entropylinear discriminant analysisau-tonomous learning multi-model 0-order
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 16-22 )