English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 conceptof intelligent maintenance, the application of intelligent systems to accomplishfault 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 updatedepends not only on a similarity measure, but also on the monitoring changes in theinput data flow. A merging cluster mechanism was incorporated into the algorithmto enable the removal of redundant clusters. Multivariate Gaussian membershipsfunctions are employed in the fuzzy rules to avoid information loss if there is interactionbetween variables. The novel approach provides greater robustness to outliersand noise present in data from process sensors. The classifier is evaluated in faultdiagnosis of a DC drive system. In the experiments, a DC drive system fault simulatorwas used to simulate normal operation and several faulty conditions. Outliersand noise were added to the simulated data to evaluate the robustness of the faultdiagnosis model

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