Condition-Based Probabilistic Safety Assessment (CB-PSA) makes use of the information made available during operation by sensors and/or inspections on the state of components and systems. This allows specializing the PSA to the conditions of the components and systems, reducing the uncertainty on the risk measures quantified. In this paper, we demonstrate the CB-PSA with reference to a spontaneous Steam Generator Tube Rupture (SGTR) accident scenario in a Pressurized Water Reactor (PWR). Results show that the updated risk measures are capable of reflecting the actual state of the SG in the tailored risk evaluation.
We present a coevolutionary optimization approach for the automatic and unsupervised extraction of industrial component degradation indicators from a set of signals collected during operation. It embeds a deep sparse autoencoder (SAE) for the extraction of the degradation indicators, into a multi-objective coevolutionary optimization algorithm, which maximizes the SAE's performance by optimizing its architecture and hyperparameters. The effectiveness of the proposed approach is shown by its application to a synthetic dataset, which mimics the operation of a degrading component in an environment affected by seasonal changes.
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