退役机床零部件多失效特征评价模型研究
Study on the evaluation model of multi-failure-features of used machine tool's parts
针对退役机床零部件多失效特征评价问题，本文提出一种多失效特征评价方法，实现了退役机床零部件多失效特征的定量评价。首先，采用故障树分析法建立机床零部件故障树并获得了失效特征和影响因素。其次，选取主要影响因素和主要失效特征建立基于径向基神经网络的机床零部件多失效特征评价模型，并采用自适应遗传算法对径向基神经网络进行优化。最后，通过将该模型应用于某型号退役滚齿机床主轴失效特征的评价研究，结果表明本文提出的模型能够较为准确评价滚齿机主轴的多失效特征，最大误差仅为8.5%。
iming at the multi-failure-features problems of used machine tools' parts, this paper proposes an evaluation method and realizes the quantitative and accurate evaluation of multi-failure-features of used machine tools' parts. Firstly, failure features and its corresponding affecting factors are obtained with the establishment of fault tree of used machine tools' parts. Secondly, a radial basis neural network optimized by adaptive genetic algorithm is proposed to model the relationship between the dominant failure features and its dominant affecting factors. Finally, the proposed method is validated by its application to the multi-failure-features evaluation of used hobbing machine tool's spindle, the results show that the method can predict the failure-feature values accurately with a maximum error of 8.5%.
曹华军、童少飞、陈海峰
机械运行、机械维修机械零件、传动装置机械制造工艺
退役机床零部件多失效特征故障树径向基神经网络自适应遗传算法
used machine tools' partsmulti-failure featuresfault tree analysisradial basis neural networkadaptive genetic algorithm
曹华军,童少飞,陈海峰.退役机床零部件多失效特征评价模型研究[EB/OL].(2013-08-29)[2025-08-23].http://www.paper.edu.cn/releasepaper/content/201308-346.点此复制
评论