A Feature Selection Approach Based on Genetic Algorithm Combined With Expanded Search Scope Applied to Bearing Fault Diagnosis Model

Abstract

Bearing is very important for motors. When a bearing fails, if the problem can be discovered and solved as early as possible, it can not only reduce the cost of repairs, but also greatly improve safety. This study proposes a machine learning-based model for diagnosing bearing faults. Regarding this model, first, the Hilbert–Huang transform (HHT) and multi-resolution analysis (MRA) in feature extraction methods are used to derive relevant features from the original signal. Then, a feature selection method based on genetic algorithm (GA) and combined with the concept of expanded search scope is used to delete redundant features. Finally, the k-nearest neighbour algorithm (KNN) and feed-forward neural network (FFNN) in the classifier are used. In addition, the University of California Irvine (UCI) datasets, Case Western Reserve University (CWRU) bearing dataset, Mechanical Failure Prevention Technology (MFPT) bearing dataset, and motor fault current signal dataset were used to validate the fault diagnosis ability of the proposed model.