Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions

Abstract

The diagnosis of mechanical and electrical faults of induction motors (IMs) has been performed using artificial neural networks (ANN) for similar, interpolated and extrapolated operating speeds. The current and vibration signals of faulty and healthy IMs measured from a Machinery Fault Simulator are used in this work. In total, ten different IM fault conditions have been considered: four mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, and bowed rotor), five electrical faults (broken rotor bar, phase unbalanced fault with two severity levels, and stator winding fault with two severity levels), and one healthy motor condition. An ANN model is developed in which raw time domain data of faulty IMs are used and the fault diagnosis is then performed for the motor’s various operating conditions. Initially, diagnosis is performed to predict and classify the motor faults, for the same operating conditions for which we trained ANN. The diagnosis is then extended for interpolated and extrapolated speeds in order to accomplish the diagnosis when data are not available at all the required operating speeds. From the results, it is found that the present ANN-based diagnosis is effective in the same speed case for various operating conditions (seven speeds as well as three loads). In addition, the diagnosis is found to be satisfactory for all interpolated and extrapolated speed cases. It is also observed that the present IM fault diagnosis is better in the interpolation speed cases than the extrapolation speed cases.