Observer-biased bearing condition monitoring: From fault detection to multi-fault classification

Chuan Li, José Valente De Oliveira, Mariela Cerrada, Fannia Pacheco, Diego Cabrera, Vinicio Sanchez, Grover Zurita

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

© 2016 Elsevier Ltd. All rights reserved. Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems.
Translated title of the contributionMonitoreo del estado de los rodamientos con sesgo de observador: De la detección de fallos a la clasificación de fallos múltiples
Original languageEnglish
Pages (from-to)287-301
Number of pages15
JournalEngineering Applications of Artificial Intelligence
DOIs
StatePublished - 1 Apr 2016

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