Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction

• Published in: IEEE transactions on industry applications 2017-05, Vol.53 (3), p.3086-3097
• Main Authors: Saucedo Dorantes, Juan Jose, Delgado Prieto, Miquel, Osornio Rios, Roque A, Romero Troncoso, Rene De Jesus
• Format: Article
• Language: English
• Subjects: Circuit faults; Condition monitoring; Discriminant analysis; Electric motors, Induction; Empirical analysis; Enginyeria dels materials; Enginyeria mecànica; Fault detection; Fault diagnosis; Feature extraction; feature reduction; Genetic algorithms; induction motor (IM); Induction motors; Industrial applications; Maintenance; Manteniment; Manteniment i reparació; Maquinària; Methodology; Motors; Motors elèctrics; Motors elèctrics d'inducció; multiple faults; Neural networks; Optimization; Principal component analysis; Principal components analysis; Reduction; Reliability aspects; Signal processing; Signal resolution; Vibrations; Àrees temàtiques de la UPC
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2016.2637307

Condition monitoring schemes are essential for increasing the reliability and ensuring the equipment efficiency in industrial processes. The feature extraction and dimensionality reduction are useful preprocessing steps to obtain high performance in condition monitoring schemes. To address this issue, this work presents a novel diagnosis methodology based on high-dimensional feature reduction applied to detect multiple faults in an induction motor linked to a kinematic chain. The proposed methodology involves a hybrid feature reduction that ensures a good processing of the acquired vibration signals. The method is performed sequentially. First, signal decomposition is carried out by means of empirical mode decomposition. Second, statistical-time-based features are estimated from the resulting decompositions. Third, a feature optimization is performed to preserve the data variance by a genetic algorithm in conjunction with the principal component analysis. Fourth, a feature selection is done by means of Fisher score analysis. Fifth, a feature extraction is performed through linear discriminant analysis. And, finally, sixth, the different considered faults are diagnosed by a Neural Network-based classifier. The performance and the effectiveness of the proposed diagnosis methodology is validated experimentally and compared with classical feature reduction strategies, making the proposed methodology suitable for industry applications.