Research on Wear life Prediction of CRDM Transmission Pair Based on Machine Learning

Control Rod Drive Mechanism (CRDM) is the only equipment unit with relative operation in the reactor. It can adjust the reactivity of the reactor quickly, so it is very important for the safe operation of the reactor. Wear is the main factor affecting the functional failure of CRDM transmission pair, directly determining its service life. Through the wear life test of the transmission pair of CRDM, it is found that the three main wear forms of the transmission pair are abrasive wear, fatigue wear and oxidation wear. At the same time, it is found that when the wear volume ratio at the top of the transmission pair reaches 16.46%, the sliding rod appears in the driving mechanism, which can be judged that the rotating parts have worn out, and the wear volume value at this moment is taken as the failure threshold of transmission pair. After obtaining the data of transmission pair wear degradation and external vibration signals, the relationship between internal wear and external vibration signals is constructed. Through external vibration signals and based on three machine learning algorithms SVR, CNN and LSTM, the life prediction models of CRDM transmission pair were established respectively. Through comparative analysis, it is concluded that in terms of prediction accuracy, the LSTM model outperforms the CNN model, which in turn outperforms the SVR model, while in terms of computational efficiency, the SVR model surpasses the CNN model, which in turn surpasses the LSTM model.