A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
AbstractDue to the traditional state recognition approaches for complex electromechanical equipment having had the disadvantages of excessive reliance on complete expert knowledge and insufficient training sets, real-time state identification system was always difficult to be established. The running efficiency cannot be guaranteed and the fault rate cannot be reduced fundamentally especially in some extreme working conditions. To solve these problems, an online state recognition method for complex equipment based on a fuzzy probabilistic neural network (FPNN) was proposed in this paper. The fuzzy rule base for complex equipment was established and a multi-level state space model was constructed. Moreover, a probabilistic neural network (PNN) was applied in state recognition, and the fuzzy functions and quantification matrix were presented. The flowchart of proposed approach was designed. Finally, a simulation example of shearer state recognition and the industrial application with an accuracy of 90.91% were provided and the proposed approach was feasible and efficient. View Full-Text
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Xu, J.; Wang, Z.; Tan, C.; Liu, X. A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network. Algorithms 2016, 9, 34.
Xu J, Wang Z, Tan C, Liu X. A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network. Algorithms. 2016; 9(2):34.Chicago/Turabian Style
Xu, Jing; Wang, Zhongbin; Tan, Chao; Liu, Xinhua. 2016. "A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network." Algorithms 9, no. 2: 34.
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