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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.

Keywords

Fault diagnosis of mechanical equipment fuzzy pattern recognition convolutional neural network fuzzy c-means clustering

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References

Venkatasubramanian

and Chan

, A neural network methodology for process fault diagnosis, AIChE Journal 35(12) (1989), 1993–2002.

Chow

M.Y.

, Mangum

P.M.

and Yee

S.O.

, A neural network approach to real-time condition monitoring of induction motors, IEEE Transactions on Industrial Electronics 38(6) (1991), 448–453.

Liu

and Gui

W.H.

, Fault diagnosis technology for complex processes, Computer Engineering and Applications (7) (2001), 11–16.

Huang

J.D.

and Wang

, Design idea of knowledge-based fault fuzzy prediction system, Computer Engineering 27(6) (2001), 93–94.

Cai

, Li

C.M.

, Liu

and Tan

X.W.

, Fault diagnosis method for rolling bearings based on local mean decomposition and K-nearest neighbor algorithm, Modern Electronic Technology 38(13) (2015), 50–52.

Jiang

J.S.

, Wang

H.Q.

, Ke

Y.L.

and Xiang

, Fault diagnosis based on LTSA and K-nearest neighbor classifier, Vibration and Impact 36(11) (2017), 134–139.

Lin

and Yang

, An improved method for face recognition based on sparse representation, International Conference on Computational and Information Sciences (2017), pp. 229–232.

Uddin

, Islam

, Khan

S.A.

, Kim

J.M.

, Sohn

S.M.

and Choi

B.K.

, Distance and density similarity based enhanced-nn classifier for improving fault diagnosis performance of bearings, Shock and Vibration (2) (2016), 1–11.

, Ding

, Chen

, Liu

and Li

, A novel characteristic frequency bands extraction method for automatic bearing fault diagnosis based on Hilbert Huang transform, Sensors 15(11) (2015), 27869–27893.

10.

Van

and Kang

H.J.

, Wavelet kernel local fisher discriminant analysis with particle swarm optimization algorithm for bearing defect classification, IEEE Transactions on Instrumentation and Measurement 64(12) (2015), 3588–3600.