A COMBINATION OF SUPPORT VECTOR MACHINE AND k-NEAREST NEIGHBORS FOR MACHINE FAULT DETECTION

被引：22

作者：

Andre, Amaury B. ^{[1
]}

Beltrame, Eduardo ^{[1
]}

Wainer, Jacques ^{[2
]}

机构：

[1] SEMEQ, Limeria, SP, Brazil

[2] Univ Estadual Campinas, Comp Inst, BR-13083852 Campinas, SP, Brazil

来源：

APPLIED ARTIFICIAL INTELLIGENCE | 2013年 / 27卷 / 01期

基金：

巴西圣保罗研究基金会;

关键词：

ARTIFICIAL NEURAL-NETWORKS; CLASSIFICATION; DIAGNOSIS;

D O I：

10.1080/08839514.2013.747370

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents a combination of support vector machine (SVM) and k-nearest neighbor (k-NN) to monitor rotational machines using vibrational data. The system is used as triage for human analysis and, thus, a very low false negative rate is more important than high accuracy. Data are classified using a standard SVM, but for data within the SVM margin, where misclassifications are more like, a k-NN is used to reduce the false negative rate. Using data from a month of operations of a predictive maintenance company, the system achieved a zero false negative rate and accuracy ranging from 75% to 84% for different machine types such as induction motors, gears, and rolling-element bearings.

引用

页码：36 / 49

页数：14

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