SAL-CNN：使用时频信息估计轴承的剩余使用寿命
SAL-CNN: Estimate the Remaining Useful Life of Bearings Using Time-frequency Information
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，对复杂系统的性能较差。提出了一种基于短时傅立叶变换（STFT）的端到端RUL预测方法。考虑到信号序列的时间相关性，在CNN中设计了长短期记忆网络，融入卷积块注意模块，从可解释性层面理解网络的决策过程。在2012年PHM数据集上进行实验，并与其他方法进行比较，结果证明了该方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
