A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks
A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks
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DOI：
10.1016/j.ress.2023.109332
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Huixin Zhang;Xiaopeng Xi;Rong Pan
中科院分区：
文献类型：
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作者：
Huixin Zhang;Xiaopeng Xi;Rong Pan
准确的剩余使用寿命（RUL）预测对预测性维修具有重要意义。随着近年来传感器技术和人工智能的进步，数据驱动的工业设备RUL预测方法得到了广泛的关注。然而，以往的研究没有充分考虑到降解速率的变化和降解过程中积累的信息。为了解决这一问题，本文提出了一种新的两阶段机器学习的规则语义预测方法。构造了一组非线性健康指标函数来指导退化过程的长短期记忆学习器的训练过程，然后利用时滞神经网络进行RUL预测。以滚动轴承数据集为例，验证了该方法在预测精度和保守性方面的优越性。
Accurate remaining useful life (RUL) prediction is of great importance for predictive maintenance. With the recent advancements in sensor technology and artificial intelligence, the data-driven approaches to RUL prediction of industrial equipment have gained a lot of attention. However, past researches have not adequately considered the variety of degradation rates and the accumulated information in degradation processes. To deal with this problem, a novel two-stage machine learning approach of RUL prediction is proposed in this paper. A set of nonlinear health indicator functions are constructed to guide the training process of a long short-term memory learner of degradation processes, then a time delay neural network is utilized for RUL prediction. The superiority of the proposed approach in terms of prediction accuracy and conservativeness is demonstrated by a case study of rolling element bearing dataset.