Self-Organizing Map and feature selection for of IM broken rotor bars faults detection and diagnosis

摘要

This paper presents a new robust and high performances fault diagnosis scheme for broken bar fault detection and severity evaluation. The aim is to ensure an accurate condition monitoring and reduced false or missed alarms rate for induction motor operating in critical applications. It investigates the combination of features selection methods with the Self-Organizing Maps (SOM) neural network in a fault detection and severity evaluation system. This approach, based on the current analysis, uses multiple features extraction techniques, where the zero crossing times (ZCT) signal and the envelope are extracted from the three-phase stator currents. Then, statistical and frequency domains features are calculated from these extracted signals. The ReliefF feature selection technique is used to select from the extracted features the most sensitive and relevant ones. Next, the SOM neural network is used as a decision-making system. The experimental investigations, conducted using a healthy machine and a machine with broken bars, show the effectiveness of the proposed fault detection technique in terms of the classification accuracy.