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.
|Number of pages||15|
|Journal||Engineering Applications of Artificial Intelligence|
|State||Published - 1 Apr 2016|
Bibliographical noteFunding Information:
The work was sponsored in part by the Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador, by the Chongqing Technology and Business University (CTBU) open Grant number: 1456027 , and by the project UID/MULTI/00631/2013 - CEOT .
© 2016 Elsevier Ltd. All rights reserved.
- Fault detection
- Fault diagnosis
- Fuzzy clustering
- Observer-biased clustering
- Wavelets packed transform