Vmd measure distance12/30/2023 Adaptive bearing fault diagnosis method of multi-channel correlation J Aerospace Power 2018 33 07 1750 1757 Google Scholar Gong G (2015) Fault feature extraction and diagnosis of rolling bearing under variable speed. Gao W Peng Y Hyperspectral image classification based on multiple kernel learning with Mahalanobis distance Chin J Sci Instrum 2018 39 03 250 257 Google Scholar Strip defect detection based on Gabor wavelet and weighted Mahalanobis distance J Electron Meas Instrum 2016 30 05 786 793 Google Scholar Bailey TL MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites Bioinformatics 2019 28 1 56 62 4039708 Google Scholar The experimental results show that the proposed method has better diagnosing performance. Finally, all feature vectors are utilized to train improved SVM, with which the fault modes of rolling bearings are identified. The model integrates the parameter solutions of the Mahalanobis distance function and the support vector machine into the same framework, which makes full use of the advantages of both and makes it easier to get the solution of the parameters. The Euclidean distance is usually used in the calculation of the Gaussian kernel function of the SVM, which cannot measure the distance between two samples accurately, so we combine the Mahalanobis distance with SVM, construct a Gaussian function kernel based on Mahalanobis distance, and propose a classifier model based on Mahalanobis distance Gaussian function kernel. Then we calculate the sample entropy of the decomposed modal component, which is considered as the feature and input of support vector machine (SVM). When raw signals are decomposed by VMD, according to the center frequency of each decomposed mode, the number of modes is selected. The vibration signals are generally non-linear, to extract feature, VMD has been employed to reconstruct signals. In this work, since the original vibration signal contains a lot of noise, we use wavelet threshold method to denoise the original vibration signal. This paper presents a novel approach to identify the rolling bearings fault based on variational mode decomposition (VMD) and Mahalanobis distance support vector machine (MDSVM). Rolling bearings are one of the most vulnerable parts in rotating machines.
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