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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Andre, Amaury B. [1] ; Beltrame, Eduardo [2] ; Wainer, Jacques [3]
Total Authors: 3
Affiliation:
[1] SEMEQ. Limeria
[2] SEMEQ. Limeria
[3] Univ Estadual Campinas. Comp Inst
Total Affiliations: 3
Document type: Journal article
Source: APPLIED ARTIFICIAL INTELLIGENCE; v. 27, n. 1, p. 36-49, 2013.
Web of Science Citations: 1
Abstract

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. (AU)