Bayesian maintenance decision optimisation based on computing the information value from condition inspections

Abstract

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

Subset simulation for optimal sensors positioning based on value of information

“…5–7 Value of Information (VoI) has been widely used to quantify the informative value of data with respect to a given decision, by evaluating the benefits, in monetary terms, that can be gained from informing the decision with data collected by sensors installed in specific positions, prior to their actual installation. 8–10…”

“…[5][6][7] Value of Information (VoI) has been widely used to quantify the informative value of data with respect to a given decision, by evaluating the benefits, in monetary terms, that can be gained from informing the decision with data collected by sensors installed in specific positions, prior to their actual installation. [8][9][10] Here, the real challenge is computational, when the VoI computation has to be repeated for many candidate sets of sensors installation positions in order to find the optimal positions set that maximizes VoI. 11,12 Indeed, if one is to position l sensors on a SSC and can consider j available candidate positions, a total of j l…”