论文标题
SAL-CNN：使用时频信息估算轴承的剩余使用寿命
SAL-CNN: Estimate the Remaining Useful Life of Bearings Using Time-frequency Information
论文作者
论文摘要
在现代工业生产中，轴承剩余使用寿命（RUL）的预测能力直接影响系统的安全性和稳定性。传统方法需要严格的物理建模，并且对于复杂系统的表现不佳。在本文中，提出了一种端到端的统治预测方法，该方法将短期傅立叶变换（STFT）作为预处理。考虑到信号序列的时间相关性，在CNN中设计了一个长期和短期的存储网络，结合了卷积块注意模块，并从解释性级别了解网络的决策过程。实验是在2012PHM数据集上进行的，并与其他方法进行了比较，结果证明了该方法的有效性。
In modern industrial production, the prediction ability of the remaining useful life (RUL) of bearings directly affects the safety and stability of the system. Traditional methods require rigorous physical modeling and perform poorly for complex systems. In this paper, an end-to-end RUL prediction method is proposed, which uses short-time Fourier transform (STFT) as preprocessing. Considering the time correlation of signal sequences, a long and short-term memory network is designed in CNN, incorporating the convolutional block attention module, and understanding the decision-making process of the network from the interpretability level. Experiments were carried out on the 2012PHM dataset and compared with other methods, and the results proved the effectiveness of the method.
