On-Line Condition Monitoring using Computational Intelligence

TL;DR

This paper introduces an online condition monitoring framework for bushings using machine learning classifiers, notably MLP, that adapts to new data and improves fault detection accuracy significantly.

Contribution

It presents an adaptive, incremental learning approach with MLP for on-line fault diagnosis, outperforming traditional classifiers in accuracy and adaptability.

Findings

MLP outperforms SVM and RBF in accuracy and training time

Testing accuracy improved from 67.5% to 95.8% with new data

Framework's confidence level averaged at 0.92

Abstract

This paper presents bushing condition monitoring frameworks that use multi-layer perceptrons (MLP), radial basis functions (RBF) and support vector machines (SVM) classifiers. The first level of the framework determines if the bushing is faulty or not while the second level determines the type of fault. The diagnostic gases in the bushings are analyzed using the dissolve gas analysis. MLP gives superior performance in terms of accuracy and training time than SVM and RBF. In addition, an on-line bushing condition monitoring approach, which is able to adapt to newly acquired data are introduced. This approach is able to accommodate new classes that are introduced by incoming data and is implemented using an incremental learning algorithm that uses MLP. The testing results improved from 67.5% to 95.8% as new data were introduced and the testing results improved from 60% to 95.3% as new conditions were introduced. On average the confidence value of the framework on its decision was 0.92.