Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

J.I. Aizpurua, V.M. Catterson, Y. Papadopoulos, F. Chiacchio, D. D'Urso
2017 Reliability Engineering & System Safety  
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
more » ... 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. (J.I. Aizpurua). • Diagnostics: if an anomaly is detected, diagnose the cause of the fault. • Prognostics: predict the likely future degradation of the asset and estimate its remaining useful life. • Operation and maintenance planning: mitigate the effects of failure and reduce unnecessary planned maintenance. PHM techniques have emerged as promising solutions for costeffective asset management and maintenance planning [14] [15] [16] . Namely, the connection between prognostics and maintenance enables updating maintenance plans with up-to-date remaining useful life (RUL) estimations [16] [17] [18] . The RUL denotes the time distance from the current prediction time, t p , to the end of the useful life (or failure time) of the system denoted EOL : Given that remaining time after t p is random, uncertainty representation mechanisms are needed to model RUL [19, 20] . Fig. 1 shows the RUL prediction concept, where = { 1 , ... , } denotes gathered data http://dx.
doi:10.1016/j.ress.2017.04.005 fatcat:n4w64bfpxneftnedysocpftala