A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Purarjomandlangrudi, Afrooz, Ghapanchi, Amir Hossein, & Esmalifalak, Mohammad (2014) A data mining approach for fault diagnosis: An application of anomaly detection algorithm. Measurement, 55, pp. 343-352.

View at publisher

Abstract

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

Impact and interest:

15 citations in Scopus
13 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 88802
Item Type: Journal Article
Refereed: Yes
Keywords: Data mining; Fault diagnosis; Machine learning; Anomaly detection; Support Vector Machine
DOI: 10.1016/j.measurement.2014.05.029
ISSN: 0263-2241
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 27 Oct 2015 03:45
Last Modified: 27 Oct 2015 03:45

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page