Motor bearing fault diagnosis method based on GJO-LSSVM

In response to the problem of insufficient adaptive ability of a single intelligent fault diagnosis model, this paper proposes a new fault classification method based on the Golden Jackal Optimization (GJO) algorithm optimized Least Squares Support Vector Machine (LSSVM). This method first utilizes adaptive noise complete empirical mode decomposition (CEEMDAN) technology to decompose and process the collected vibration signals, thereby obtaining a series of intrinsic mode components (IMFs). Subsequently, effective components were selected from these IMFs based on screening criteria, and the energy and waveform coefficients of these effective components were extracted to jointly form the feature set for fault diagnosis. Next, the GJO algorithm is used to Perfect the selection of kernel parameters and penalty parameters in the least squares support vector machine LSSVM, thereby establishing a GJO-LSSVM fault diagnosis model. Finally, input the feature vector matrix into the GJO-LSSVM fault diagnosis model for fault diagnosis and analysis.