School of Mechanical Engineering,Liaoning Technical University Shenyang Aerospace University

为了保障综合机械化采煤工作面安全生产,需对液压支架可靠性进行评估和预测,基于煤机行业可靠性工程起步较晚,前期大量的无故障工作时间数据记录不全等现状,提出在定时截尾试验获得的失效数据为小样本的情况下,采用BP神经网络模型对失效样本及其经验可靠度进行学习、仿真来扩大样本容量,并根据机械产品可靠性一般服从威布尔分布的特点,提出使用扩容样本对威布尔分布参数进行估计的方法。研究结果表明:两参数威布尔分布适用于描述液压支架可靠性,根据其可靠度函数和失效率函数可以获得任意时刻液压支架的可靠度和失效率;样本数据较小时,使用BP神经网络进行样本容量扩充可以获得较高精度的估计参数。

In order to ensure the safety production of the fully-mechanized coal mining,an evaluation and prediction were conducted o the reliability of the hydraulic powered suppor.Based on the reliablity engineering of the cal machinery industry late started and a lot of falure-fre work time data was lost,the paper provided that the fault data from the timing censored test were under the condition of the smal sample.The BP neural network model was aplied to the study and simulation on the failure samples and experience reliability to enlarge the sample volume.Acording to the reliability of the mechanical products generally would obey Weibuldistribution features,the paper had a study of the volume enlargedsample applied to the estimation method of the Wleibul distribution parameters. The study showed that the Weibul distribution of the two parameters would be suitable to deseribe the reliablity of the hydraulic powered support and according to thereliability function and the failure rate functon,the reliability and failure rate of the hydraulic powered support could be obtained at any time.When the sample data were small.the BP neuralnetwork could be applied to the enlargement of the sample volume and would have the high accurate estimated parameters.

辽宁省工业企业科技特派行动合作资助项目(2013KTP011)；辽宁省教育厅科学研究资助项目(L2012120)；