Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors

Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.     

On-Line Fast Motor Fault Diagnostics Based on Fuzzy Neural Networks

An on-line method was developed to improve diagnostic accuracy and speed for analyzing running motors on site.On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network(FNN)as well as for network training to reduce the effects of various static factors,such as unbalanced input power and asymmetrical motor alignment,to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data...