Combination of Discriminant Analysis and Minimum Redundancy Maximum Relevance for Induction Motor Fault Diagnosis Using Stator Current Signals

Induction motors are critical components in various industrial applications. Any faults can seriously affect the production system. Therefore, early fault detection is essential to prevent such occurrences. This research aims to develop a fault diagnosis model for induction motors. Raw signal data were obtained experimentally in the laboratory using two identical three-phase induction motors. There are eight different conditions categorized into single-combined faults. 18 features were extracted from each signal, consisting of 12 time-domain features and 6 frequency-domain features. These features were selected using the minimum Redundancy maximum Relevance (mRmR) algorithm. The selected features were then used as input to build a model using the Discriminant Analysis. The results indicate that the Discriminant Analysis model achieved very high accuracy across all condition classes. The computation time of the developed model is exceptionally fast, even below one second. Quadratic Discriminant Analysis (QDA) proved to be more accurate than Linear Discriminant Analysis (LDA) in classifying faults data.