Affiliations: [a] School of Mechanical and Electrical, Xijing University, Xi’an, China
| [b] School of Teaching Researc, Xijing University, Xi’an, China
| [c] College of Automotive Engineering, Jilin Engineering Normal University, Changchun, Jilin, China
Correspondence:
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Corresponding author. Wanqiang Qi, College of Automotive Engineering, Jilin Engineering Normal University, Changchun 130052 Jilin, China. E-mail: [email protected].
Abstract: With the rapid development of modern industry and science and technology, mechanical equipment has become larger, faster and more intelligent. In real life, there is no absolutely safe and reliable equipment, so it is impossible to require mechanical equipment not to break down in the operation process, and the working environment of mechanical equipment is complex and harsh, aging is serious, and breakdowns occur frequently. Research on effective intelligent fault detection methods has become a theoretical hot spot of current discipline research. Intelligent fault diagnosis of mechanical equipment is based on the algorithm to analyze the problems of equipment fault. In this paper, a fault detection model of mechanical equipment is proposed based on the method of fuzzy pattern recognition, and the fault detection is classified by the method of Fuzzy C-Means clustering. In this paper, the method of mechanical equipment fault detection based on Convolutional Neural Network is compared with the method proposed in this paper. The experimental results show that the model has good performance in fault detection and has strong practicability.
