Multi-core Accelerated Discriminant Feature Selection for Real-Time Bearing Fault Diagnosis

This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.