Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion

Changping Li, Xiaohui Wang, Longchen Duan and Bo Lei
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Changping Li: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Xiaohui Wang: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Longchen Duan: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Bo Lei: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China

Energies, 2022, vol. 15, issue 10, 1-12

Abstract: It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods are 25.5% and 9.5%, respectively. In this paper, the discharge circuit prediction model based on Bayesian fusion is established, which improves the prediction accuracy and reliability of the model, and it guides the selection of process parameters and the design of pulse power supply and electrode bits.

Keywords: electro-pulse boring; bayes; model fusion; parameter identification; prediction model (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: 2022
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