Condition-Based Probabilistic Safety Assessment (CB-PSA) makes use of inspections and monitoring information on Systems, Structures, and Components (SSCs) to update risk quantities. In this paper, we show the benefits of exploiting the condition-based estimates for taking maintenance decisions on a SSC undergoing multiple degradation mechanisms. To develop the method, we make reference to a spontaneous Steam Generator Tube Rupture (SGTR) Accident Scenario in a Nuclear Power Plant (NPP). The SG is susceptible to multiple degradation mechanisms, i.e., Stress Corrosion Cracking (SCC) and pitting. Tube plugging and Water Lancing and Chemical Cleaning (WL-CC) can be performed, before leading to a SGTR accident. Decisions must be taken on the maintenance strategy to perform at each inspection cycle. Results of a case study regarding SGTR show that the decisions based on the risk estimates provided by a CB-PSA approach allow controlling the SGTR risk at minimum maintenance cost.

The problem of sensor positioning for condition monitoring of Systems, Structures and Components (SSCs) has been recently proposed to be addressed by Value of Information (VoI) optimization. VoI is a metric that quantifies the benefit of taking a measurement prior to adopting it. This metric lacks the characteristics of sub-modularity, i.e. the benefit of adding a measurement to a small set of measurement is higher than adding it to a bigger set. This causes the VoI optimization to not guarantee optimal results when the problem is solved by greedy optimization algorithms. In this work, the sub-modularity issue is considered with reference to the thickness gauge sensor positioning on a Steam Generator (SG) of Nuclear Power Plant (NPP), and ways forward to overcome the sub-modularity issue are suggested.

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In this work, we apply a simulation-based framework that makes use of the Value of Information (VoI) for identifying the optimal spatial positioning of sensors on pressurized equipment. VoI is a utility-based Figure of Merit (FoM) which quantifies the benefits/losses of acquiring information. Sensors are typically positioned on pressurized equipment in line with specific recommendations based on operational experience, like UNI 11096 in Italy. We show that the recommendations in UNI 11096 are, indeed, justified and that, incidentally, relying on VoI for the optimization of the sensor positioning, one can achieve the same monitoring performance, as measured by VoI, where following UNI 11096, but with a reduced number of sensors. The proposed VoI-based approach can, thus, be used to confirm or revise recommendations coming from operational experience.

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