On-Line Condition Monitoring using Computational Intelligence
  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.
