**6. Conclusions**

In this work, we have considered signal reconstruction models for fault detection in nuclear power plants. In order to improve the performance of the AABKR and extend its applicability to steady-state operating conditions, we have proposed a modification, based on a different procedure for the determination of the time position index, the position of the nearest vector within the memory vector to the query vector observation, which provides the input to the weighted-distance algorithm that captures temporal dependencies in the data. Two different approaches based on DTW for time position index identification, have been developed. The basic idea is that, the use of derivative in AABKR, which becomes constant and nearly zero during steady-state operation when the process change in time is negligible and makes it impossible to identify the time position index correctly, can be completely eliminated while maintaining an acceptable performance in monitoring during both steady-state and transient operations.

The modified AABKR method has been applied, first, to a typical steady-state process and, then, to a case study concerning the monitoring of a reactor coolant system of a PWR NPP during start-up transient operation. We have conducted Monte Carlo simulation experiments to critically examine the fault detection capability of the proposed method and the results have been compared to those of AAKR and AABKR using several performance metrics. The obtained results have shown that the reconstructions provided by the modified AABKR are more robust than those of AAKR and AABKR, in particular, during steady-state operations. The method can, then, be used for signal reconstruction during both steady-state and

transient operations, with the benefit of eliminating the use of derivatives entirely while maintaining an acceptable performance. If these approaches are adopted, the cause of abnormalities can be identified, proper maintenance intervention can be planned and earlier mitigation can be allowed to avoid the risk of catastrophic failure.

The future works will focus on (1) the development of an ensemble model in order to benefit from the exploitations of different capabilities of the three methods for signal reconstructions; and (2) the development of a method for on-line updating of the memory data, allowing the model to automatically adapt to the changes in different operating conditions.
