Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection

Abstract

The emergence of complex machinery and equipment in several areas demands efficient fault diagnosis methods. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. According to the concept of intelligent maintenance, the application of intelligent systems to accomplish fault diagnosis from process historical data has been shown to be a promising approach. In problems involving complex nonstationary dynamic systems, an adaptive fault diagnosis system is required to cope with changes in the monitored process. In order to address fault diagnosis in this scenario, use of the so-called “evolving intelligent systems” is suggested. This paper proposes the application of an evolving fuzzy classifier for fault diagnosis based on a new approach that combines a recursive clustering algorithm and a drift detection method. In this approach, the clustering update depends not only on a similarity measure, but also on the monitoring changes in the input data flow. A merging cluster mechanism was incorporated into the algorithm to enable the removal of redundant clusters. Multivariate Gaussian memberships functions are employed in the fuzzy rules to avoid information loss if there is interaction between variables. The novel approach provides greater robustness to outliers and noise present in data from process sensors. The classifier is evaluated in fault diagnosis of a DC drive system. In the experiments, a DC drive system fault simulator was used to simulate normal operation and several faulty conditions. Outliers and noise were added to the simulated data to evaluate the robustness of the fault diagnosis model.