Reliability and fatigue life analysis of key parts of shearer. (August 2022)

• Record Type: Journal Article
• Authors: Tian, Zhen; Gao, Shan; Jing, Shuangxi; Li, Jin
• Is Part Of: Engineering failure analysis. Volume 138(2022)
• Publication Date: 2022-08
• Subjects: Shearer -- Reliability -- PSO-BP neural network -- Fatigue life -- Rain flow counting; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106357
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: The stress information of the rocker arm shell, planetary carrier and planetary shaft was obtained through simulation. The main factors affecting the reliability of shearer's key parts were analyzed by orthogonal simulation test and PSO-BP algorithm. The fatigue life of key parts of shearer were predicted by rain flow counting processing of stress information. Abstract: The poor reliability of shearer restricts the development and production efficiency of thin coal seam. The dynamic characteristics of the key parts have a great influence on the reliability and fatigue life of the thin seam shearer. In order to achieve high-yield and high-efficiency production of thin coal seams, the instantaneous load on the spiral drum was calculated based on the mechanical model of the spiral drum during the cutting process and the coal sample properties. According to the structure of the shearer and the material of key parts, a rigid-flexible coupling model of the shearer was established, and the stress information of the rocker arm shell, planetary carrier and planetary shaft was obtained by dynamic simulations. The membership function between material stress and structural reliability was constructed by Gaussian membership function, the main factors affecting the reliability of shearer were analyzed by orthogonal simulation test and PSO-BP neural network. The fatigue life of rocker arm shell, planetary carrier and planetary shaft were predicted by counting the rain flow of the stress information. Measures to improve the reliability and fatigue life of the shearer were proposed based on the analysis results, and effectiveness of the method proposed in this paper was verified by the practical application.