Accession Number:

AD1007974

Title:

Evolving Fuzzy Classifier based on Clustering Algorithm and Drift Detection for Fault Diagnosis Applications

Descriptive Note:

Conference Paper

Corporate Author:

Federal University of Minas Gerais Belo Horizonte Brazil

Report Date:

2014-12-23

Pagination or Media Count:

10.0

Abstract:

Nowadays, in several areas, efficient fault diagnosis methods for complex machinery and equipments are required. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. In general, these methods use mathematicalstatistical models, accumulated experience, or even process historical data to perform fault diagnosis. Although methods based on models or experience have shown to be effective, they have the disadvantage of requiring previous knowledge of the dynamic system in question. On the contrary, methods based on process historical data do not require a prior knowledge, they are based solely on data obtained directly from the dynamic system. The application of so-called Evolving Intelligent Systems to accomplish fault diagnosis from process data have been shown a promising approach. This paper proposes an evolving fuzzy classifier based on a new approach that combines a recursive clustering algorithm and a drift detection method and its application on dynamic systems fault diagnosis. The novel approach provides greater robustness to outliers and noise present in data from process sensors. The classifier is evaluated in fault diagnosis of an interacting tank system and the results are promising.

Subject Categories:

  • Manufacturing and Industrial Engineering and Control of Production Systems
  • Mechanics
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE