An improved fault diagnosis approach based on support vector machine

Qi Zhao, Bingqian Wang, Gan Zhou, Wenfeng Zhang, XiuMei Guan, W. Feng
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引用次数: 1

Abstract

Fault diagnosis is extremely important for guaranteeing safe and reliable operation of modern industrial process. As an active branch of fault diagnosis, data-driven methods attract more and more attention in recent years, because they solely depend on information collected in historical databases. The support vector machine (SVM), aims at minimizing the structural risk, exhibits superior generalization ability, and succeeds in the nonlinear classification problem. This paper proposes an improved SVM based fault diagnosis framework, which consists of two primary components: (1) feature extraction; (2) classification. More specifically, multi-scale principal component analysis (MSPCA) is performed to achieve multi-scale analysis and dimension reduction. Classification combines SVM classifier with parameters optimization method, which further encompasses grid search (GS) and particle swarm optimization (PSO). To demonstrate the accuracy and efficiency of our approach, we perform experiments on the classical Tennessee Eastman (TE) process.
基于支持向量机的改进故障诊断方法
故障诊断对于保证现代工业过程安全可靠运行具有极其重要的意义。数据驱动方法作为故障诊断的一个活跃分支,由于其完全依赖于历史数据库中的信息,近年来受到越来越多的关注。支持向量机(SVM)以最小化结构风险为目标,表现出优越的泛化能力,在非线性分类问题中取得了成功。本文提出了一种改进的基于支持向量机的故障诊断框架,该框架由两个主要部分组成:(1)特征提取;(2)分类。具体来说,通过多尺度主成分分析(MSPCA)实现多尺度分析和降维。分类将支持向量机分类器与参数优化方法相结合,进一步包含网格搜索和粒子群优化。为了证明我们的方法的准确性和效率,我们在经典的田纳西伊士曼(TE)过程上进行了实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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