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