Model-based incipient fault diagnosis - multi-step neuro-predictors and multiresolution signal processing

Timely detection and diagnosis of incipient faults is desirable for online condition assessment purposes. In this paper, a model-based fault diagnosis system is developed for induction motors, using recurrent neural networks for multistep transient response prediction and multiresolution signal processing for nonstationary signal feature extraction. The proposed diagnosis system uses only measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. Scaling of the diagnosis system to machines with different power ratings is demonstrated with data from 2.2 kW, 373 kW and 597 kW induction motors.