Introduction  With the digital development of nuclear power, more and more nuclear power equipment data can be collected, and the operation and maintenance personnel can obtain the operation conditions of each equipment through data analysis. Accurate operation condition classification of nuclear power equipment is the basis for realizing the health assessment and anomaly discovery of nuclear power equipment. However, due to the wide variety of sensors inside the nuclear power equipment, the amount of data to be analyzed is too large, which brings great challenges to the manual classification of the operation conditions of nuclear power equipment. To achieve accurate and rapid automatic classification of nuclear power equipment operation conditions, this paper proposes a self-supervised learning algorithm for nuclear power operation condition classification based on TICC clustering.

  Method  Firstly, the historical operation data of nuclear power equipment was normalized, and the elbow method was used to determine the optimal cluster number. Then the TICC clustering algorithm was used to classify the historical operation data of nuclear power equipment, and the data fragments of each condition were labeled by the classification results. Finally, the labeled condition data was used to train the convolutional neural network to obtain the condition classification model. Ultimately, the real operation data of nuclear power equipment was used for verification.

  Result  The experimental results show that the classification accuracy of the proposed algorithm is 96.6%, and the classification needs only 3.2 seconds.

  Conclusion  Compared with the K-means algorithm and TICC algorithm, the algorithm proposed in this paper has a great improvement in accuracy and classification speed, and the algorithm can effectively help nuclear power operation and maintenance personnel complete the classification of operating conditions of nuclear power equipment.