Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Aizpurua, J. I.; Catterson, V. M.; Chiacchio, F.; D'Urso, D.; Papadopoulos, Y.; Papadopoulos, Yiannis; Aizpurua, Jose Ignacio; Catterson, Victoria; Chiacchio, Ferdinando; D'Urso, Diego

Abstract

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry.

Citation

Papadopoulos, Y., Aizpurua, J. I., Catterson, V. M., Chiacchio, F., D'Urso, D., Papadopoulos, Y., Aizpurua, J. I., Catterson, V., Chiacchio, F., & D'Urso, D. (2017). Supporting group maintenance through prognostics-enhanced dynamic dependability prediction. Reliability Engineering and System Safety, 168, 171-188. https://doi.org/10.1016/j.ress.2017.04.005

• Journal Article Type: Article
• Acceptance Date: Nov 4, 2017
• Online Publication Date: Apr 21, 2017
• Publication Date: Dec 1, 2017
• Deposit Date: May 23, 2017
• Publicly Available Date: May 23, 2017
• Journal: Reliability engineering and system safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 168
• Pages: 171-188
• DOI: https://doi.org/10.1016/j.ress.2017.04.005
• Keywords: Prognostics, Predictive maintenance, Diagnostics, Dynamic dependability, Maintenance grouping
• Public URL: https://hull-repository.worktribe.com/output/451559
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0951832016308626
• Additional Information: This article is maintained by: Elsevier; Article Title: Supporting group maintenance through prognostics-enhanced dynamic dependability prediction; Journal Title: Reliability Engineering & System Safety; CrossRef DOI link to publisher maintained version: http://dx.doi.org/10.1016/j.ress.2017.04.005; Content Type: article; Copyright: © 2017 The Authors. Published by Elsevier Ltd.
• Contract Date: May 23, 2017