Approximate estimation of fatigue life of hydraulic support
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摘要: 针对常规的液压支架寿命近似分析方法需对危险点进行循环加载和获取，导致计算机负载增加的问题，提出了一种基于遗传算法与BP神经网络的寿命估算模型。利用遗传算法的全局搜索性优化BP神经网络，使其不易陷入局部最小点；利用优化后的BP神经网络建立危险点结构参量到疲劳寿命的网络映射模型。针对样本容量和隐含层节点数进行了测试，测试结果表明，样本容量为40、隐含层节点数为7时，模型估算精度较高；液压支架平均寿命估算值为36 456次，与理论值的最大相对误差为5.27%。Abstract: In view of problem that conventional life approximate analytical method of hydraulic support needs cyclic loading and acquisition of dangerous points which leads to increase of computer load, a life estimation model based on genetic algorithm and BP neural network was proposed. The BP neural network is optimized by using global search performance of genetic algorithm to avoid falling into the local minimum points. The optimized BP neural network is used to establish network mapping model between structural parameters of the dangerous points and fatigue life. The test results of sample size and number of hidden layer nodes show that the estimation accuracy of the model is high when the sample size is 40 and the number of hidden layer nodes is 7; the average value of estimated life of hydraulic support is 36 456 times, and the maximum relative error with the theoretical value is 5.27%.
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Keywords:
- hydraulic support /
- fatigue life /
- estimation model /
- genetic algorithm /
- BP neural network
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期刊类型引用(6)
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