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Fine-tuning the hyperparameters of the Support Vector Machine (SVM) is crucial to making it an accurate classifier in fault detection. The convergence rate of the optimiser and its simplicity are also vital for achieving the optimal boundary. Therefore, a highly accurate fault detection algorithm that combines SVM with a simple, easy-to-implement, and stable optimiser called Projection Recurrent Neural Network (PRNN) is proposed in this paper. The algorithm focuses on detecting bearing faults in induction motors using experimentally measured stator currents. The initial dataset is pre-processed using Discrete Wavelet Transform (DWT), Power Spectral Density (PSD), and cepstrum analysis. A feature set is then derived from several statistical metrics, and Kernel Principal Component Analysis (KPCA) is used to select the most salient features. The SVM is trained using these discriminative features, and its optimisation problem is reformulated as Constrained Nonlinear Programming (CNP). The PRNN is introduced to solve the CNP to effectively determine the optimal decision boundary of the SVM. The study evaluates the accuracy of the proposed algorithm and highlights the advantages of using PRNN in SVM, its dependence on the type of kernel function and the selected features. A 4 kW induction motor is used as a prototype machine for experimental data collection under healthy and faulty conditions. The results show that the proposed algorithm leads to more accurate fault detection compared to the conventional SVM and recent fault detection methods.
Hosseini et al. (Thu,) studied this question.