A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

The main objective of this study is to propose a motor fault diagnosis model based on machine learning. Compared with the traditional motor fault diagnosis model, the proposed model can reduce the computation time. This model can be divided into three steps: feature extraction, feature selection, and classification. In the feature extraction step, the original signal is extracted by Hilbert-Huang transform (HHT), envelope analysis (EA), and variational mode decomposition (VMD) methods. A feature selection method based on memory space computation genetic algorithm (MSCGA) is proposed and applied in the feature selection step. The advantage of MSCGA is that it eliminates the need to compute data fitness values, saving unnecessary computation time repeatedly. The classifiers use k-nearest neighbor (KNN) and support vector machines (SVM). In order to verify the stability and efficiency of the model, the university of California Irvine (UCI) benchmark dataset, the current signal of motor fault datasets, and case western reserve university (CWRU) were used. The UCI dataset is used to test the efficiency and computation time of the feature selection method. Other datasets are used to compare with traditional motor fault diagnosis models. The simulation results of the proposed model have demonstrated the effectiveness in reducing the computation time without affecting the computation results compared to the traditional motor fault diagnosis model. Furthermore, the performance of MSCGA is proven to be better than that of the other algorithm.