An Association Rule Mining-Based Method for Revealing the Impact of Operational Sequence on Nuclear Power Plants Operating

Abstract

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators.