Abstract
Due 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.
Highlights
With the rapid development of modern industry, electromechanical devices are developing towards the direction of large scale and complex structures
A Fuzzy Probabilistic Neural Network (FPNN) combines the existing expert knowledge, fuzzy control theory and probabilistic neural network organically, and it is widely used in prediction, classification and fault diagnosis
In order to tackle the above problems, this paper proposes a novel multi-sensor information fusion method for state recognition through integration of fuzzy logic theory and a probabilistic neural network
Summary
With the rapid development of modern industry, electromechanical devices are developing towards the direction of large scale and complex structures. State recognition is the process of identification and classification of the equipment working condition. In this process, appropriate features are extracted to describe each part of the object, and it is divided into different states according to the characteristics [4]. Multiple sensors are installed on complex equipment to record information of different scales and levels [6]. Traditional state recognition methods mainly combine the field sensor information according to the existing knowledge and experience [7]. Bearing the above observation in mind, we apply a fuzzy probabilistic neural network (FPNN) to solve the problem of state recognition for complex equipment, and the rest of this paper is organized as follows.
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