A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

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...

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Bibliographic Details
Published inEnergies (Basel) Vol. 16; no. 2; p. 956
Main Authors Liu, Min, Liu, Zhiqi, Cui, Jinyuan, Kong, Yigang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2023
Subjects
Accuracy
Algorithms
Approximation
Artificial intelligence
Back propagation networks
Cavitation
Coal mining
Efficiency
Fault diagnosis
hydraulic heightening system
Hydraulics
Methods
Neural networks
Radial basis function
Rough set models
RS-RBFNN
simulation
Systems stability
Topology
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16020956