SENSORS & MONITORING | PREDICTING RPV LIFE


received from the sensors combines both system and sensor degradation and is the only data available. The observed data is categorised into two distinct segments. The initial 20 data points represent the iron atom displacement observed over 20 operating cycles, equivalent to fuel cycles. The subsequent data points project into the future, spanning intervals of 32, 35, 40, 48, 54, and 60 EFPY. Considering use of the maximum likelihood estimation (MLE) method for estimating system and sensor parameters, the quantity of available data points assumes critical significance. In general, as the sample size (number of data points) increases, MLE tends to provide more accurate and reliable parameter estimates. To address this issue, the authors employed interpolation techniques, enabling additional data points to be inserted to increase the density of data including an additional data point representing the initial state, where the iron atom displacement is zero at time zero. Comparing the real data path with the simulated Wiener


Process shows the real dataset closely adheres to the characteristics of a Wiener process. Since actual sensor degradation data was not available the authors employed simulated data to represent sensor degradation with a drift parameter η set to a value of 3.00 × 10−4 and diffusion parameter δ set to 7.00 × 10−4. The simulated sensor degradation data was then subtracted from the observed data to obtain the system degradation data. The plot illustrates the observed measurements, simulated sensor degradation data, and calculated system degradation data and suggests that all three data paths adhere to a Wiener process. The system state estimation achieved through the


Below, figure 1: Measurement and prediction data with the predefined threshold indicated


application of the Kalman filter provides a comparison between the degradation levels obtained from the measurement data and the estimation. The authors argue that the close resemblance signifies the effectiveness of this approach but add that it is worth noting that the slight disparities can be attributed to variances in the true and estimated parameters, given the uncertainty associated with the real parameter values.


More accurate useful life prediction The subsequent degradation measurement and prediction of system failure over time is shown in Figure 1, below, which illustrates the actual degradation measurements of a system component as a function of time, along with a predefined failure threshold. The intersection of the degradation curve with the failure threshold line marks the critical point where the component is assumed to have failed, which in this example case occurs at approximately 15 EFPY with a degradation level of 0.015 dpa. This is the point at which the system may require maintenance or replacement. The failure threshold is a pre-specified value that defines when the system is considered to have failed and the remaining useful life (RUL) is the amount of time that the system is expected to remain operational. The RUL Kalman filter equations developed by the authors can be used to predict the probability that the system will fail within a certain period of time. This information can then be used to make informed decisions about system maintenance and replacement. For example, if the probability of the system failing within the next week is 10%, scheduling a maintenance inspection may be prudent. Or, if the probability of the system failing within the next year is 50%, we may want to start planning for a replacement. This study tackles a critical and often-neglected factor in


0.025


Measurement Failure threshold


0.020


RPV health assessment: the effects of sensor degradation on RUL predictions. By meticulously quantifying this impact, the research addresses a hidden vulnerability in conventional RPV monitoring and underscores the need for degradation-aware predictive models. In this case the Wiener process is applied to the nuclear domain, modelling the stochastic deterioration of RPVs. This marks a departure from deterministic models, enabling a more realistic representation of the complex and often unpredictable degradation mechanisms at play in harsh nuclear environments. The utilisation of the Wiener process offers flexibility that accommodates the complexities of degradation under nuclear reactor conditions, providing a significant advance over previous modelling approaches. Applying adaptive Kalman filter algorithms for RUL estimation in nuclear power plants (NPPs) tackles the unique challenges of NPP monitoring, allowing for dynamic refinement of RUL predictions as new sensor data becomes available. By harnessing real-world surveillance capsule data this empirical foundation significantly enhances the robustness and reliability of these degradation models, bridging the gap between theory and the complex realities of nuclear component aging. In this work the authors demonstrate the importance


0.015 (15.03,0.015) 0.010 0.005 0.000 0 510 15 Total time (EFPY) 32 | January 2025 | www.neimagazine.com 20 25


of incorporating sensor degradation into remaining useful life (RUL) predictions for critical nuclear reactor pressure vessels. The findings also quantify the substantial errors that sensor degradation introduces into RUL estimations. By explicitly modelling sensor degradation in tandem with neutron embrittlement, the paper outlines the influence of sensor deterioration on nuclear component health evaluations. The authors note that future research endeavours will be to investigate sensor fusion techniques to further enhance the robustness of health assessments. Additionally, the integration of sensor degradation models into a comprehensive predictive maintenance framework promises to transform nuclear industry maintenance practices. ■


Degradation (dpa)


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