Reliability Analysis and Optimization of Forage Crushers Based on Bayesian Network

doi:10.23940/ijpe.23.10.p7.700709

Abstract

Abstract: Forage crushers are used to process forage into soft filaments to improve the feeding intake rate of the forage and livestock digestibility. High failure rate and low reliability of the whole machine during forage-crushing operations are a concern. To improve the reliability of crushers, the Bayesian network model was applied to evaluate the reliability of forage crushers and clarify the root cause of forage crushers failure. Multi-Island genetic algorithm was used for multi-objective optimization according to the main causes of crusher failures. The study revealed that the main causes of the failures of forage crushers were resonance caused by uneven wear of hammers, the insufficient fatigue strength of throwing blades or hammers, and wear failure of the hammers. After optimization, the reliability of the forage crusher increased from 0.739 to 0.912, which satisfied the reliability requirements of forage crushers. This study can provide a reference for the fault maintenance and reliability optimization design of forage crushers.

Key words: bayesian network, fault tree, reliability analysis, multi-objective optimization

Jinxin Wang, Zhiping Zhai, Yuezheng Lan, Xiaoyi Zhai, and Lixiang Zhao. Reliability Analysis and Optimization of Forage Crushers Based on Bayesian Network [J]. Int J Performability Eng, 2023, 19(10): 700-709.

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