粒子群优化的支持向量机在截割部行星齿轮减速器故障诊断中的应用

THE APPLICATION OF PSO-SVM IN FAULT DIAGNOSIS OF CUTTING UNIT PLANETARY GEAR

采煤机截割部行星轮减速器是否故障,关系着采煤机能否顺利采煤,首先采集了减速器多种故障时的信号,然后,运用粒子群算法对支持向量机的相关参数进行优化,得到最优的分类器,最后,运用支持向量机分类器对采集到的各种信号进行处理,结果表明,该方法能够准确快速地对减速器的各种故障进行诊断,该方法的提出有助于对采煤机截割部行星轮减速器故障进行实时自动地诊断。

If the fault is found in planetary gear of cutting unit in the shearer,the shearer will not be successful mining.In this paper,first,a variety of failure signal of reducer is collected and the parameters of support vector machine are optimized using PSO and the optimal classifier is obtained,second the collected various signals are processed using classifier of Support Vector Machines.The results show that this method can diagnose the various fault of reducer quickly and accurately.The proposed method is contribute to achieve fault diagnosis of planetary gear real-time and automatically.