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

摘要

This article jrresenls a combination of support, vector machine (SVM) and h-nearesl 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 misclassi-ficalions are more like, a, k-NN is used to reduce the false negative rale. 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.