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Proceedings Paper

A review of fault prognostics in condition based maintenance
Author(s): Lei Zhang; Xingshan Li; Jinsong Yu
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Paper Abstract

The main idea of condition based maintenance (CBM) is to monitor the health of critical machine components and system almost continuously during operation and maintenance actions based on the assessed condition. If done correctly, CBM has the benefits such as reducing catastrophic failures, minimizing maintenance and logistical cost, maximizing system security and availability and improving platform reliability. A CBM system usually has four functional modules: feature extraction, diagnostics, prognostics and decision support. Among them, fault prognostics is the most important enabling technology. It is the most challenging research area which is so called crystal ball of CBM. But it has the potential to be the most beneficial one. A review of recent progress of fault prognostics is conducted with the emphasis placed on its algorithmic approaches. These approaches generally fall into four main categories, namely experience-based approaches, model-based approaches, knowledge-based approaches and data-driven approaches. Based on the analysis of some typical examples on each prognostic approaches, the advantages and disadvantages of these approaches are further discussed. Finally, the future challenges concerned with fault prognostics are also presented.

Paper Details

Date Published: 6 November 2006
PDF: 6 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 635752 (6 November 2006); doi: 10.1117/12.717514
Show Author Affiliations
Lei Zhang, Beijing Univ. of Aeronautics and Astronautics (China)
Xingshan Li, Beijing Univ. of Aeronautics and Astronautics (China)
Jinsong Yu, Beijing Univ. of Aeronautics and Astronautics (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence
Jiancheng Fang; Zhongyu Wang, Editor(s)

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