Abstract：A smart system predicting the remaining useful life (RUL) of fan belts can ensure the safety of chemical engineering processes and reduce human labor. In this study, the features extracted from the vibration signals of the belt are fed into a stacked autoencoder (SAE) to obtain the health indicator (HI). Then, a polynomial is fitted using the HI to calculate the RUL of the belt. The destructive experiment is conducted to shorten the data collection period, and a loss function is constructed to fit the destructive experiment. This study also improved the structure of the SAE, i.e., the inner product and the activation operation of the middle layer are separately done in two neurons. This bounds the HI in [0,1] and preserves more information from the encoding layers. Compared with traditional SAEs, the proposed method reduces the prediction errors in real cases. The errors of the predicted RUL are bounded by 1 d in the last 50% of the sections of all three datasets. The trained model also performs well when employed on an online test.
赵泽恒, 赵劲松. 基于破坏性实验和自动编码器的风机皮带剩余寿命预测[J]. 清华大学学报（自然科学版）, 2022, 62(9): 1458-1466.
ZHAO Zeheng, ZHAO Jinsong. Remaining useful life prediction of fan belts based on destructive experiments and autoencoders. Journal of Tsinghua University(Science and Technology), 2022, 62(9): 1458-1466.
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