Remaining useful life prediction for multi-sensor systems using a novel end-to-end deep-learning method
Remaining useful life prediction for multi-sensor systems using a novel end-to-end deep-learning method
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DOI：
10.1016/j.measurement.2021.109685
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发表时间：
2021-09
期刊：
影响因子：
5.6
通讯作者：
Yuyu Zhao;Yuxiao Wang
中科院分区：
文献类型：
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作者：
Yuyu Zhao;Yuxiao Wang
剩余使用寿命（RUL）预测在保证现代工程系统的可靠性和安全性方面起着至关重要的作用。对于复杂系统，传统的RUL预测方法的间接方式限制了其通用性和准确性。实现准确的RUL估计的挑战在于直接探索RUL与来自多个监测传感器的大量数据之间的潜在关系。受此启发，本文提出了一种基于深度学习模型的端到端RUL预测方法。该模型采用长短期记忆（LSTM）编解码器作为模型的主框架，处理多变量时间序列数据。在此基础上，提出了一种两阶段注意机制，实现了对输入特征和时间相关性的自适应提取和评价。在此基础上，通过多层感知器得到RUL预测。该模型能够在不需要任何先验知识的情况下有选择地关注关键信息，这对提高RUL预测精度具有重要意义。通过某型涡扇发动机数据集验证了该方法的有效性和优越性，并与现有方法进行了比较。
Remaining useful life (RUL) prediction plays a crucial role in ensuring reliability and safety of modern engineering systems. For complicated systems, the indirect manner of the conventional RUL prediction approaches restricts their universality and accuracy. The challenge to realize accurate RUL estimation consists in the direct exploration of the potential relationship between the RUL and the numerous data from multiple monitoring sensors. Motivated by this fact, a novel end-to-end RUL prediction method is proposed based on a deep learning model in this paper. The long short-term memory (LSTM) encoder-decoder is employed as the main frame of the model to deal with multivariate time series data. Then a two-stage attention mechanism is developed to realize adaptive extraction and evaluation of the input features and temporal correlation. On this basis, the RUL prediction is obtained by a multilayer perceptron. The proposed model can selectively focus on the critical information without any prior knowledge, which is of great significance to enhance the RUL prediction accuracy. The effectiveness and superiority of the proposed method is experimentally validated through a turbofan engine dataset and compared with the state-of-the-art methods.