A fault diagnosis method based on hybrid sampling algorithm with energy entropy under unbalanced conditions

Abstract

For the degraded performance of the fault diagnosis model caused by massive normal samples and scarce fault samples under unbalanced conditions, a new fault diagnosis method based on a hybrid sampling algorithm and energy entropy, namely HSEEFD is proposed in this paper. In the proposed method, Empirical Modal Decomposition (EMD) is employed to decompose the vibration signals into Intrinsic Mode Functions (IMFs), and the energy entropy feature of each IMF component is extracted to construct a feature vector matrix. Then, a new hybrid sampling algorithm using Tomek’s Links algorithm, Euclidean distance, K-means algorithm, and synthetic minority over-sampling technique (SMOTE), namely TSHSA is designed to balance the extracted features. Tomek’s Links algorithm is used to identify and remove the confusable majority class samples at the boundary. Euclidean distance is applied to find the suspected noise points in minority class samples and remove them. The k-means algorithm is employed to cluster the minority class samples and SMOTE is used to deal with each cluster according to the density of the clusters to synthesize new features. Finally, the support vector machine (SVM) is applied to classify faults and realize fault diagnosis. The experiment results on the actual imbalanced data show that the proposed HSEEFD method can effectively improve the accuracy of the fault diagnosis under unbalanced conditions by increasing the accuracy value by more than 2.1%, and the AUC and G-mean by more than 0.7%, 2.1%, respectively.

Small sample uncertainty evaluation of industrial robot position accuracy measurement based on grey model

“…principle analysis SMOTE is an oversampling method [20], which is mainly to solve the problem of data imbalance. It improves the balance of data by linear interpolation between minority samples to obtain synthetic samples.…”