SVM practical industrial application for mechanical faults diagnostic

Authors:
Lane Maria Rabelo Baccarini;Valceres Vieira Rocha E Silva;Benjamim Rodrigues De Menezes;Walmir Matos Caminhas
Affiliations:
Department of Electrical Engineering, Federal University of São João del Rei, Praça Frei Orlando, 170 - Centro - 36307-352, Minas Gerais, Brazil and Department of Electronics Engine ...;Department of Electrical Engineering, Federal University of São João del Rei, Praça Frei Orlando, 170 - Centro - 36307-352, Minas Gerais, Brazil and Department of Electronics Engine ...;Department of Electrical Engineering, Federal University of São João del Rei, Praça Frei Orlando, 170 - Centro - 36307-352, Minas Gerais, Brazil and Department of Electronics Engine ...;Department of Electrical Engineering, Federal University of São João del Rei, Praça Frei Orlando, 170 - Centro - 36307-352, Minas Gerais, Brazil and Department of Electronics Engine ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine's vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In this paper, both, machine's vibrations and SVM are used together for a new intelligent mechanical fault diagnostic method. Using only one vibration sensor and only four SVM's it was achieved improved results over the available approaches for this purpose in the literature. Therefore, this method becomes more attractive for on line monitoring without maintenance specialist intervention. Vibration signals turns out to occur in different directions (axial, horizontal or vertical) depending on the type of the fault. Thus, to diagnose mechanical faults it is necessary to read signals at various positions or use more them one accelerometer. From this work we also determined the best position for signals acquisition, which is very important information for the maintenance task.