智能化的风机皮带寿命预测系统有助于确保化工安全,同时减少人工劳力。该文提取振动信号的特征,输入多层自动编码器得到健康指标(HI),通过多项式拟合HI即可预测剩余寿命。为缩短数据采集周期,开展了破坏性实验,并构建了适合破坏性实验的损失函数。同时,基于多层自动编码器的结构进行了改进,将中间层的内积与激活函数运算分离计算,限定HI为[0,1]的同时保留更多编码层信息。与传统自动编码器方法相比,中间层分离的自动编码器在实际问题中降低了预测误差,实现了3组数据后50%区段预测误差不超过1 d的良好结果,上线测试同样表现良好。
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.
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