Research on wear life prediction of CRDM transmission pair based on machine learning

Control Rod Drive Mechanism(CRDM) is the only unit with relative operation in 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 that affects the failure of the driving pair of the control rod drive mechanism, and directly determines its service life. Through the wear life test of the driving pair of CRDM, it is found that the three main wear forms of the driving 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. After obtaining the data of wear degradation and external vibration signals, the relationship between internal wear and external vibration signals is constructed, based on three machine learning algorithms SVR, CNN and LSTM, the life prediction models of control rod drive mechanism were established, LSTM model is superior to CNN model in prediction precision, and SVR model is superior to CNN model in computation efficiency.