Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Abstract

Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

Neighborhood Ranking-Based Feature Selection

“…FNN is considered a method capable of supporting feature selection [31], and has been used to select the appropriate embedding dimension [12] and to detect determinism [32]. Practical applications include diagnosing bearing failure, where the FNN selects parameters that indicate malfunctioning operation [33]. Batteries have been analyzed by determining the minimum embedding dimensions, which are sent to a hybrid neural network to calculate the remaining lifetime [34].…”