悬链式船舶链条机械磨损寿命预测准确度评估
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摘要
为了提高悬链式船舶链条机械磨损寿命预测准确性能,设计了一种基于改进神经网络的悬链式船舶链条机械磨损寿命预测模型。首先对悬链式船舶链条机械磨损寿命预测研究现状进行分析,找到引起悬链式船舶链条机械磨损寿命预测精度的因素,然后收集悬链式船舶链条机械磨损寿命的历史数据,并采用BP神经网络建立悬链式船舶链条机械磨损寿命预测模型,并引入自适应遗传算法对BP神经网络的不足进行改进,最后采用仿真实验对悬链式船舶链条机械磨损寿命预测模型的准确度进行评估,结果表明,改进神经网络提高了悬链式船舶链条机械磨损寿命预测精度,预测结果比其他模型更加可靠。
In order to improve the accurate performance of the mechanical wear life prediction of the suspended chain ship chain, a model for predicting the wear life of the suspended chain ship chain based on improved neural network is designed. Firstly, the research status of mechanical wear life prediction of suspended chain ship chain is analyzed, and the factors that cause the prediction accuracy of the mechanical wear life of the chain ship chain are found. Then, the historical data of the wear life of the suspended chain ship chain machinery is collected, and the BP neural network is used to predict the wear life of the chain. The model is introduced, and the adaptive genetic algorithm is introduced to improve the shortage of BP neural network. Finally, the simulation experiment is used to evaluate the accuracy of the suspended chain ship chain mechanical wear life prediction model, and the result is more love. The improved neural network improves the prediction accuracy of the suspension chain ship chain mechanical wear life, and the prediction results are compared with that of the model. The model is more reliable.
In order to improve the accurate performance of the mechanical wear life prediction of the suspended chain ship chain, a model for predicting the wear life of the suspended chain ship chain based on improved neural network is designed. Firstly, the research status of mechanical wear life prediction of suspended chain ship chain is analyzed, and the factors that cause the prediction accuracy of the mechanical wear life of the chain ship chain are found. Then, the historical data of the wear life of the suspended chain ship chain machinery is collected, and the BP neural network is used to predict the wear life of the chain. The model is introduced, and the adaptive genetic algorithm is introduced to improve the shortage of BP neural network. Finally, the simulation experiment is used to evaluate the accuracy of the suspended chain ship chain mechanical wear life prediction model, and the result is more love. The improved neural network improves the prediction accuracy of the suspension chain ship chain mechanical wear life, and the prediction results are compared with that of the model. The model is more reliable.
引文
[1]王成處,刘家竣,韦淡平,等.摩擦学材料及表面工程[M],北京:国防工业出版社,2012.
[2]石莹,江亲瑜,李宝良.圆柱滚子轴承磨损及可靠性寿命的数值仿真[J].润滑与密封,2011,36(2):30-33.
[3]廖映华,张良栋,李志荣,王春.基于动力学的行星齿轮传动系统的轴承磨损寿命预测[J].2013,37(10):41-44.
[4]赵迎祥,魏宗平.滚动轴承磨损寿命数据的灰色预测[J].机械制造,2010,48(11):66-68.
[5]刘占文,赵祥模,李强,等.基于图模型与卷积神经网络的交通标志识别方法[J].交通运输工程学报,2016,16(5):122-131.
[6]周莉,王珏,周勇等.遗传算法在试卷生成中的应用[J].江西师范大学学报(自然科学版),2013(11):579-583.
[2]石莹,江亲瑜,李宝良.圆柱滚子轴承磨损及可靠性寿命的数值仿真[J].润滑与密封,2011,36(2):30-33.
[3]廖映华,张良栋,李志荣,王春.基于动力学的行星齿轮传动系统的轴承磨损寿命预测[J].2013,37(10):41-44.
[4]赵迎祥,魏宗平.滚动轴承磨损寿命数据的灰色预测[J].机械制造,2010,48(11):66-68.
[5]刘占文,赵祥模,李强,等.基于图模型与卷积神经网络的交通标志识别方法[J].交通运输工程学报,2016,16(5):122-131.
[6]周莉,王珏,周勇等.遗传算法在试卷生成中的应用[J].江西师范大学学报(自然科学版),2013(11):579-583.