FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP

ZHAO LiJuan; ZHANG Bo; ZHANG Wen

doi:10.16579/j.issn.1001.9669.2021.04.030

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FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP

更新时间：2022-09-22

- FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP
- Journal of Mechanical Strength Vol. 43, Issue 4, Pages: 977-981(2021)
- 作者机构：
  
  1. 辽宁工程技术大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2021.04.030
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ZHAO LiJuan, ZHANG Bo, ZHANG Wen. FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP. [J]. 43(4):977-981(2021)
DOI：

ZHAO LiJuan, ZHANG Bo, ZHANG Wen. FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP. [J]. 43(4):977-981(2021) DOI： 10.16579/j.issn.1001.9669.2021.04.030.

摘要

为对采煤机截割部行星架进行疲劳寿命分析及预测,建立采煤机的刚柔耦合模型,研究了行星架的动力学特性,得到了其最大应力值并以此作为输入,利用n Soft得到了行星架的疲劳寿命,利用BP、PSO-BP和改进的PSO-BP对不同工况下的行星架寿命进行预测。研究表明:夹矸坚固性系数为8.4,截深600 mm,转速90 r/min,牵引速度2.5 m/min的工况下,行星架的最大应力为554.781 2 MPa,应力集中区域为花键退刀槽处,疲劳寿命为5.507 2×106; BP、PSO-BP和改进的PSO-BP三种神经网络模型中,预测疲劳寿命最大相对误差分别为4.35%、2.52%和0.90%,迭代次数分别为23、8和6次,改进PSO-BP模型既提高了预测精度,也提高了迭代速度。融合Matlab、Ansys、Adams、n Soft和改进的PSOBPNN,为工矿装备关键零件的疲劳寿命预测提供了方法。

Abstract

In order to analyze and predict the fatigue life of the shearer’s cutting planet carrier,the shearer’s rigid-flexible coupling model was established,the planet carrier’s dynamic characteristics was studied,and the planet carrier’s maximum stress value was obtained as the input. The planet carrier’s fatigue life was obtained by using n Soft,and the planet carrier’s fatigue life,under different working conditions was predicted by using BP,PSO-BP and improved PSO-BP. The results show that under the condition of solidity coefficient of 8. 4,depth of cut-off 600 mm,rotational speed of 90 r/min and traction speed of 2. 5 m/min,the maximum stress is 554. 78 MPa,the planet carrier’s stress concentration area is at the spline receding groove,and the fatigue life is 5. 507 2×106. Among the three neural network models,BP,PSO-BP and the improved PSO-BP,the predicted fatigue life’s maximum relative error is 4. 35%,2. 52% and 0. 90%,and the iteration ’s number is 23,8 and 6 times,respectively. The improved PSO-BPNN model improves the prediction accuracy and also improves the iterative speed. Combining MATLAB、ANSYS、ADAMS、n Soft and improved PSO-BPNN,this paper provides a method for predicting the fatigue life of key parts of industrial and mining equipment.

关键词

采煤机行星架疲劳寿命自适应变异粒子群算法BP神经网络

Keywords

ShearerPlanet carrierFatigue lifeAdaptive variationParticle swarm optimization algorithmBP neural network

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