Along with wide application of sensors, multi-dimensional time-series data are commonly available for remaining useful life (RUL) estimation. This paper proposes a joint data-driven approach that adapts two models, AdaBoost regression and Long Short-Term Memory (LSTM), to estimate the RUL based on data trajectory extension. In RUL prediction, the data trajectories in the training set contain the data up to the units’ failure while the data trajectories in the testing set do not. Although this fact has a significant negative effect on the accuracy of RUL estimation, it is considered by few literatures. The proposed approach adapts the LSTM to learn the time series dependencies of training data and then extend the trajectories of testing data, aiming at reducing the variance of the lengths of data trajectory between the training and testing sets. Then, the proposed approach adapts the AdaBoost regression to estimate the RUL using the extended time series data. The proposed approach is competitive with state-of-the-art methods by demonstrating on two degradation datasets.

随着传感器的广泛应用，多维时间序列数据通常可用于剩余使用寿命（RUL）估计。本文提出了一种联合数据驱动的方法，该方法适用于两个模型AdaBoost回归和长期短期记忆（LSTM），以基于数据轨迹扩展来估计RUL。在RUL预测中，训练集中的数据轨迹包含直到单元故障为止的数据，而测试集中的数据轨迹则没有。尽管这个事实对RUL估计的准确性有很大的负面影响，但很少有文献考虑它。所提出的方法使LSTM适应于学习训练数据的时间序列依赖性，然后扩展测试数据的轨迹，旨在减少训练和测试集之间数据轨迹长度的差异。然后，提出的方法使AdaBoost回归适应使用扩展的时间序列数据来估计RUL。通过演示两个降级数据集，所提出的方法与最新方法具有竞争力。