Abstract
Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals
Highlights
Induction motors represent the majority of the drive means in industrial installations
Induction motor health-condition-monitoring system is very crucial for maintenance engineers and the development of reliable instrumentation and robust diagnostic techniques are very useful for the success of a predictive maintenance plan for several industries
Vibration signals acquisition Experiments were carried out on a test rig designed for the creation of several types of mechanical faults; the faults of the induction motors were maintained for the realization of several tests of the healthy motor, with broken rotor bars, with bearing faults and with rotor misalignment
Summary
Induction motors represent the majority of the drive means in industrial installations. Inference-based intelligent methods include various techniques which can be used independently or combined to improve their efficiency Some of these techniques use expert systems [9], fuzzy logic [10], artificial neural networks (ANNs) [11], Bayesian inference [12], genetic algorithms (GA) [13], and SVM [14], etc. To achieve the objectives of the study, a Multi Layer Perceptron (MLP) ANN will be structured and optimized by L27 Taguchi design for online monitoring of induction motors. In order to have an AI system usable on line, the DOE based on the L27 Taguchi design, were chosen to determine the best structure of the ANN at the level of the hidden layers, and the best combination of activation functions Based on their sensitivity to induction motors faults, the vibration indicators calculated from the signals measured in time domain were selected.
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