Impact of propagation of fault signals on industrial diagnosis using current signature analysis

Diagnosis of the significant events in electrical equipments is a challenging research area. Motor current signature analysis provides good results in laboratory environment. In real life situation electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors may ease out identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks.