A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network
Min Liu (liumin@sxist.edu.cn),
Zhiqi Liu,
Jinyuan Cui and
Yigang Kong
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Min Liu: College of Intelligent Manufacturing Engineering, Shanxi Institute of Science and Technology, Jincheng 048000, China
Zhiqi Liu: College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Jinyuan Cui: College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Yigang Kong: College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Energies, 2023, vol. 16, issue 2, 1-15
Abstract:
The hydraulic heightening system is the core component of the shearer, and its stable operation directly affects the safety and reliability of the equipment, so it is of great significance to realize an efficient and accurate fault diagnosis. This paper proposes a fault diagnosis method combining a rough set and radial basis function neural network (RS-RBFNN). Firstly, the RS is used to discretize the original fault data set and attribute reduction, remove the redundant information, and mine the implicit knowledge and potential rules. Then, the topology structure of the RBFNN is determined. The mapping relationship is established between the fault symptom and category. The fault diagnosis is carried out with Python language. Finally, the method is compared with two diagnostic methods including a back propagation neural network (BPNN) and RBFNN. The research results show that the RS-RBFNN has the highest fault diagnosis accuracy, with an average of 98.68%, which verifies the effectiveness of the proposed fault diagnosis method.
Keywords: hydraulic heightening system; fault diagnosis; RS-RBFNN; simulation; accuracy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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