基于故障树和混沌粒子群算法的锻压机床故障诊断方法

锻压机床由于生产效率高和材料利用率高的特点,被广泛应用于各领域.然而,锻压机床发生故障时,其故障种类繁多、故障数据量大,所以对锻压机床故障源的快速、准确诊断较困难.针对该问题,文章提出一种将故障树分析法和混沌粒子群算法相融合的方法,对锻压机床的故障源进行故障诊断.该方法是先通过故障树分析法对锻压机床的故障进行分析从而得到故障模式及其故障概率,然后由得到的故障模式和已知的故障维修经验分析归纳出故障模式的学习样本,再根据得到的故障概率运用混沌粒子群算法的遍历性快速、准确地诊断出锻压机床发生故障的精确位置.文章提出的方法以锻压机床的伺服系统为例进行了故障诊断实验,将该实验结果与遗传算法、粒子群算法进行对比.实验结果表明,文章的算法在锻压机床伺服系统的故障诊断中准确度更高、速度更快.

Fault Diagnosis Method of Forging Machine Based on Fault Tree and Chaos Particle Swarm Algorithm

Forging machine is widely used in various fields due to its high production efficiency and high utilization rate of material.However,when the forging machine breaks down,it is very difficult to diagnose the fault source of the machine because of the wide variety of faults and the large amount of fault data.To solve this problem,a new diagnostic method combining a fusion algorithm of fault tree and chaotic particle swarm algorithm was proposed,using which the fault source of forging machine can be diagnosed quickly and accurately.Firstly,the fault mode and probability of the forging machine are obtained by fault tree analysis,and then the learning samples of the fault pattern can be summarized according to the fault pattern and the known fault maintenance experience.Finally,the exact location of forging machine faults can be determined quickly using the probability of faults and the ergodic of the particle swarm optimization algorithm.The fault diagnosis experiments were performed on the servo system of forging machine by using the proposed method,and the experimental results was compared with that obtained both from the genetic algorithm and the particle swarm optimization algorithm.It was found that the proposed method has higher accuracy and faster speed in fault diagnosis of the servo system of forging machine.