基于支持向量机的提升机制动系统故障诊断  

Mine Hoist Braking System Fault Diagnosis Based on a Support Vector Machine

作　　者：郭小荟[1] 马小平[1] 

机构地区：[1]中国矿业大学信息与电气工程学院

出　　处：《中国矿业大学学报》2006年第6期813-817,共5页Journal of China University of Mining & Technology

基　　金：江苏省自然科学基金项目(BK2003026)

摘　　要：针对提升机制动系统中常见的卡缸故障,利用支持向量机(SVM)这一新的机器学习方法进行智能诊断．在某一闸系统正常时获得2组信号,卡缸时获得6组信号,采用3层小波包对闸瓦间隙-时间信号进行分解,以各频带的能量为元素构造特征向量,形成故障诊断样本,在Mat- lab6．5环境下用SVM工具箱进行编程,建立SVM故障分类器并对测试样本进行测试,从而实现提升机制动系统卡缸故障诊断．实验结果表明,在不到0．1 s时间内,就建立了SVM故障分类器,该分类器对测试样本的诊断正确率达到了100％;当训练样本由6组减少至4组时,SVM故障分类器仍可以有效地实现对卡缸故障的诊断．因此,SVM方法对于少样本的故障诊断有较强的适应性,非常适合于矿井提升机这种安全运行要求很高,但又不具备大量故障样本的系统．The blockage piston in cylinder, a typical fault of mine hoist braking system, was intelligently diagnosed with a support vector machine（SVM） method. Gathering two sets of signals while in normal working and six sets of signals while in blockage piston in cylinder, using a wavelet package of 3 levels to decompose signal curves of brake distance-time, and construct feature vectors with the energy of all individually frequency bands signal, and form the fault di- agnosis samples. Based on SVM toolbox in Matlab6.5 system, SVM fault classifier was constructed for testing samples and the blockage piston in cylinder fault diagnosis was completed. Experimental results show that the SVM fault classifier was constructed within 0. 1 second and the testing samples diagnosis correct rate of this classifier is up to 100%. It can also effectively accomplish the blockage piston fault diagnosis of braking system when the training samples are decreased from 6 sets to 4 sets. Hence, the SVM method has a high adaptability for fault diagnosis in the case of smaller number of samples and is especially suitable for mine hoist.

关 键 词：小渡包 支持向量机 提升机 制动系统 故障诊断 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]