采用改进型DENCLUE和SVM的电子皮带秤故障诊断

Fault diagnosis of belt weigher using the improved DENCLUE and SVM

针对电子皮带秤的在线故障诊断问题,提出一种基于改进型DENCLUE聚类分析和偏二叉树支持向量机(SVM)分类器的故障在线检测和诊断方法.由于故障数据随设备流量的变化而变化,采用改进DENCLUE聚类算法对实时检测到的数据进行聚类分析,分离出故障数据,实现在线故障检测;将DENCLUE算法中的密度估计方法引入到支持向量机中,提出一种基于类内相似密度和类间相似密度构建可分性测度和二叉树结构的改进型BTSVM,结合标准数据集验证了改进型BTSVM的优越性,并利用该分类器对检测出的故障进行故障模式在线识别诊断.对阵列式皮带秤进行试验,结果表明,提出的故障在线检测和诊断模型更适合散状物料连续称重系统的在线故障检测诊断.

A method of on-line fault detection and diagnosis based on the modified DENCLUE clustering and partial binary tree support vector machine（ SVM） is proposed for on-line fault diagnosis problem of bulk weighing equipment—electronic belt weigher. Firstly,in view of the fault data varying with equipment flow,a modified DENCLUE clustering algorithm is designed to realize the online fault detection by isolating the fault data after the clustering analysis of the real-time data. Secondly,the density estimation method in DENCLUE algorithm is introduced into the support vector machine, and then an improved BTSVM, in which the separability measure and binary tree structure is built based on the similar density within class and between class,is presented to recognize the detected fault on-line. The improved BTSVM is also verified the superiority by the standard dataset. Finally,the proposed online fault detection and diagnosis model is verified more suitable for the online fault detection and diagnosis of bulk weighing equipment by the array belt weigher experiments.