Affiliations: [a]
School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, P.R. China | [b]
Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai, P.R. China | [c]
School of Engineering, Sports and Sciences, University of Bolton, Bolton, UK
Correspondence:
[*]
Corresponding author. Zhang Hong-Li, School of Mechatronics Engineering and Automation, Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai, P.R. China. Tel.: +86 21 56331523; E-mail: [email protected].
Abstract: An accurate and efficient intelligent fault diagnosis method plays a key role in reducing the production arrest of forthcoming faults in modern industrial machines, increasing the safety of plant operations and optimizing manufacturing costs. Recently, a new approach for hierarchical clustering based on data field, was put forward and obtained good effect. Thus, inspired by the principle, a new efficient and intelligent fault diagnosis method called Mass Optimizing Group Identification Classification Algorithm (MOGICA) has been proposed in this article. In this classifier, the classification rate and size of used objects population have some fluctuation with the change of only parameter δ. Thus, with the purpose of making data field distribution more reasonable and increasing the classification accuracy, Entropy is introduced to determine the parameter δ. The performance of the method has been tested through two kinds of experiments. In the first experiment, four benchmark data sets were used to evaluate the performance of this algorithm. In the second experiment, the algorithm was used to diagnose the faults of ball bearing. Compared with other classification techniques in the two experiments, our method is more competitive.
Keywords: Data field, MOGICA, quadratic programming, classification, fault diagnosis
