Intelligent Systems Applied to the Classification of Multiple Faults in Inverter Fed Induction Motors

The monitoring condition of electrical machine is an important parameter for maintenance of industrial process operation levels. In this paper, an investigation based on learning machine classifiers to proper classify machine multiple faults i.e. stator short-circuits, broken rotor bars and bearings in three phase induction motors driven by different inverters models is proposed. Experimental tests were performed in 2 different motors, running at steady state, operating under variable speed and torque variation resulting in 2967 samples. The main concept of proposed approach is to apply the three phase current amplitudes to immediately detect motor operating conditions. The high dimensionality of the input vectors in the algorithms was solved through the discretization of the current data, which allows the reduction the classification complexity providing a optimized waveform in comparison with the original one. The results show that it is possible to classify accurately these faults.