FAULT DEVELOPMENT PROCESS IN ELECTRICAL DRIVES: A REVIEW

This work studied the use of neural networks for model based fault diagnostics of induction motors. The idea was to use data provided by accurate FEM simulations of motor operation under different fault and load conditions to create identification based ne ural network model models which can be used for online fault diagnosis. The neural network models are created for each phase of the three phase induction motor, and each load condition is handled separately. Most of the faults cases have to be left out due to the unavailability of data for different fault conditions. The data is often a restriction for data - based models, because each different situation would require its own set of data, along with data for validation and testing. In literature in one of th e work a FEM model of a 35 kW induction motor was simulated for cases of a healthy, rotor faulted and stator faulted motor. The parameters for the model were obtained from a real operational motor. No real measurement data was used, but studies with FEM si mulations of induction motors show that the data is very realistic with respect to the data obtained from real motor measurements [Arkkio 1990]. Measurement data from a real motor was, however, used as an input to the FEM models in the data group that was used to build the time series models. Induction motors are one of the commonly used electrical machines in industry because of various technical and economical reasons. These machines face various stresses during operating conditions. These stresses might lead to some modes of failures/faults. Hence condition monitoring becomes necessary in order to avoid catastrophic faults .