Detecting Bearing Faults in Line-Connected Induction Motors Using Information Theory Measures and Neural Networks

Fault detection in electrical machines is a topic widely explored by researchers, especially bearing faults that represent about half of the total three-phase induction motor failure occurrences. This kind of fault is detectable by specific frequencies of the stator current and is a wide source of investigation. Thus, this work presents a predictability analysis method that provides patterns based on measures of relative entropy, Bhattacharyya distance, and Lempel–Ziv complexity estimated over reconstructed signals obtained from wavelet packet decomposition components. The signals under study were collected from motors with faults in the inner or outer races, which were artificially created in laboratory. These patterns were applied to three neural network topologies, which were used to classify the signals into two groups: normal or faulty.