Dynamic and gradual coupled reliability analysis of the transmission system of a shearer cutting arm

Zhu, Lisha; Yuan, Cong; Li, Huanjun; Huang, Jing

doi:10.1177/1748006x211042462

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…CHEN et al [2] used nonlinear dynamics and Runge-Kutta method to predict the reliability of shearer gear transmission system. ZHU et al [3] used probability perturbation theory and the fourth-order moment method to design the shearer cutting arm. MA et al [4] revealed the failure mechanism of the walking wheel and improve the haulage reliability of the shearer.…”

Section: Introductionmentioning

confidence: 99%

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

Zhipeng

2023

Matéria (Rio J.)

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Shearer reliability is considered as one of the most important indexes in longwall mining production. However, the traditional reliability methods are based on the specific distribution of the failure parameters, which are incongruent in the actual practice. Therefore, a novle shearer reliability prediction method based on support vector machine (SVM) with chaotic particle swarm optimization (CPSO) is proposed. It combines the advantages of the high accuracy of SVM and the fast convergence of CPSO, where the chaos idea is introduced to particle swarm optimization for the particle initialization, inertia weight coefficient optimizing and premature convergence treatment. Then this CPSO is used to select and optimize the important parameters of SVM. Ultimately, the optimized parameters are used to obtain a superior CPSO-SVM method for reliability prediction.To show the effectiveness of the proposed method, two numerical comparisons are designed respectively using the literature data and the actual shearer data from the coal mine enterprise. The research results reveal the prediction accuracy and validity of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

Zhipeng

2023

Matéria (Rio J.)

View full text Add to dashboard Cite

show abstract

“…Chen [2] used nonlinear dynamics and Runge-Kutta method to predict the reliability of shearer gear transmission system. Zhu [3] used probability perturbation theory and the fourth-order moment method to design the shearer cutting arm. Ma [4] revealed the failure mechanism of the walking wheel and improve the haulage reliability of the shearer.…”

Section: Introductionmentioning

confidence: 99%

Shearer Reliability Prediction Using Support Vector Machine Based on Chaotic Particle Swarm Optimization Algorithm

Zhang

2022

Preprint

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Shearer reliability is considered as one of the most important indexes in longwall mining production. However, the traditional reliability methods are based on the specific distribution of the failure parameters, which are incongruent in the actual practice. Therefore, a novle shearer reliability prediction method based on support vector machine (SVM) with chaotic particle swarm optimization (CPSO) is proposed. It combines the advantages of the high accuracy of SVM and the fast convergence of CPSO, where the chaos idea is introduced to particle swarm optimization for the particle initialization, inertia weight coefficient optimizing and premature convergence treatment. Then this CPSO is used to select and optimize the important parameters of SVM. Ultimately, the optimized parameters are used to obtain a superior CPSO-SVM method for reliability prediction. To show the effectiveness of the proposed method, two numerical comparisons are designed respectively using the literature data and the actual shearer data from the coal mine enterprise. The research results reveal the prediction accuracy and validity of the proposed method.

show abstract

Dynamic and gradual coupled reliability analysis of the transmission system of a shearer cutting arm

Cited by 2 publications

References 28 publications

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

Shearer Reliability Prediction Using Support Vector Machine Based on Chaotic Particle Swarm Optimization Algorithm

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