Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

• Published in: Reliability engineering & system safety 2017-12, Vol.168, p.171-188
• Main Authors: Aizpurua, J.I., Catterson, V.M., Papadopoulos, Y., Chiacchio, F., D'Urso, D.
• Format: Article
• Language: English
• Subjects: Case studies; Condition monitoring; Diagnostics; Dynamic dependability; Electric industries; Internet; Maintenance; Maintenance grouping; Occupational health; On-line systems; Predictions; Predictive maintenance; Preventive maintenance; Prognostics; Reliability aspects; Reliability engineering; Repair & maintenance; System dynamics; System effectiveness; System reliability; Systems health monitoring
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2017.04.005

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. •System maintenance strategies are upgraded to use dynamic failure logic models.•A maintenance grouping strategy is proposed using prognostics and criticality indices.•Sensitivity of the proposed method is assessed for different dependability models.•Asset maintenance strategies increase dependability, but also maintenance actions.•System maintenance strategies reduce maintenance and repair actions.