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

作者：Andre Amaury B; Beltrame Eduardo; Wainer Jacques^*

来源：Applied Artificial Intelligence, 2013, 27(1): 36-49.

DOI：10.1080/08839514.2013.747370

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.

• 出版日期2013