Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD–TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD–TCN model is improved by 10–20% than traditional convolutional algorithms.

中文翻译：

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用于剩余使用寿命估计的经验模式分解和时间卷积网络

剩余使用寿命（RUL）预测在现代工业中对保证安全运行和降低维护成本具有重要作用。在本文中，我们提出了一种新的基于时间经验模式分解 (EMD) 和时间卷积网络 (TCN) 的 RUL 估计深度学习方法。所提出的框架可以有效地揭示轴承退化信号的非平稳特性，并通过经验模态分解获取时间序列退化信号，即固有模态函数。此外，特征信息被用作卷积层的输入，并由 TCN 训练以预测剩余使用寿命。与公共数据集上的几种最先进的卷积算法相比，所提出的 EMD-TCN 模型结构保持了优越的结果。