accurate monitoring, caused by incorrect sensor readings, can directly impact RUL estimation. Despite the critical role of sensors in monitoring system health, the impact of degraded sensors on system health monitoring has been relatively overlooked. Monitoring sensor performance and assessing the impact of their health condition on observation data is vital. The presence of sensor degradation complicates the task of obtaining accurate information on the health of monitored components like RPVs, highlighting the importance of robust sensor health management in monitoring systems. Improvements in the prediction of RUL for nuclear
components have advanced understanding of their longevity and reliability by focusing on developing machine learning models and artificial intelligence techniques. However, a notable gap in these studies is the lack of consideration for sensor degradation and their dependency on extensive historical training data, which is challenging to obtain in the nuclear industry context. Using Kalman Filter (KF) for RUL prediction is a well-
established practice as it can effectively address multiple limitations in current methods of estimating RUL for RPVs. KFs accurately model dynamic and non-linear systems by integrating system equations that capture underlying physical processes, overcoming the challenges of inaccurate representation. Additionally, KFs efficiently use limited training data, employing techniques like transfer learning to mitigate extensive data requirements typical in machine learning approaches. The authors consider neutron embrittlement as the primary cause of degradation in RPV materials and model this degradation as a mathematical Weiner Process, also called Brownian motion. Neutron embrittlement is a process affected by radiation damage and various environmental variables, which results in a complex, non-linear, and non-monotonic degradation pattern for RPV steel. Neutron embrittlement is a multifaceted phenomenon characterized by radiation damage, microstructural alterations, and changes in material properties due to the continuous absorption of neutrons over time. This degradation process is profoundly influenced by variables such as neutron fluence, temperature, and the detailed composition of the steel. The interplay of these factors results in a degradation
pattern that is inherently non-linear and non-monotonic, making it challenging to model using simplistic continuous processes. These complexities necessitate advanced modelling approaches that can capture the nuanced behaviour of RPV steel under the influence of neutron embrittlement. However, for the sake of computational simplicity, the authors opted for a Wiener Process-based model, which while fundamentally simplistic, possesses the versatility to encapsulate non-linear and non-monotonic degradation patterns observed in neutron-embrittled RPV steel.
The study aimed to develop a sensor degradation model
and integrate that with Remaining Useful Life prediction algorithms, producing a framework for comprehensive health evaluation of nuclear systems, structures, and components by explicitly quantifying the impact of sensor health deterioration. The RPV is used as a critical case study to illustrate this framework by modelling the effects of neutron embrittlement under the harsh operational conditions of nuclear power plants.
Modelling sensor decay to predict RPV life Having developed an appropriate model for sensor degradation and integrated that within an RUL algorithm a real-life case study was used to provide a practical demonstration of the methodology. The data was collected from a report on the results of the
examination of Capsule W which monitors the effects of neutron irradiation on the Ameren-owned Callaway Unit 1 in Missouri, USA. Capsule W is the fifth removed from the reactor and tested in a continuing surveillance programme of reactor pressure vessel materials under actual operating conditions. Capsule W was removed at 25.75 Effective Full Power Years (EFPY) and post-irradiation mechanical tests of the Charpy V-notch and tensile specimens were performed. The report presents a detailed analysis to determine the neutron radiation environment within both the reactor pressure vessel and surveillance capsules. In the analysis, fast neutron exposure parameters in terms of fast neutron fluence (E >1.0 MeV) and iron atom displacements (dpa) were established on a plant- and fuel-cycle-specific basis. The authors used the iron atom displacement data for degradation analysis. The observed degradation data
www.neimagazine.com | January 2025 | 31
Above: More accurate predictions of RPV remaining useful life could transform industry maintenance practices Source: EDF
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