The prediction of remaining useful life (RUL) has attracted much attention, and it is also a key section for predictive maintenance. In this study, a novel hybrid deep learning framework is proposed for RUL prediction, where a variational autoencoder (VAE) and time‐window‐based sequence neural network (twSNN) are integrated. Among it, VAE is used to extract the hidden and low‐dimensional features from the raw sensor data, and a loss function is designed to extract useful data features; by using a sliding time window, twSNN can predict RUL dynamically; meanwhile, it can simplify the network architecture in the time dimension. Furthermore, to achieve higher performance on various failure conditions, long short‐term memory (LSTM) cell and convolutional LSTM (ConvLSTM) cell are designed for twSNN respectively. A case study is completed with a dataset of aircraft turbine engines. It is found that the proposed frameworks with LSTM cell and ConvLSTM cell have better performance on both single failure mode and multiple failure modes. The results also show that the prediction accuracy is averagely improved by 6.65% for single failure mode and 15.05% for multiple failure modes respectively.

中文翻译：

---

通过变分自动编码器和基于时间窗口的序列神经网络进行剩余使用寿命预测

剩余使用寿命（RUL）的预测引起了很多关注，这也是预测性维护的关键部分。在这项研究中，提出了一种用于RUL预测的新型混合深度学习框架，其中集成了变分自动编码器（VAE）和基于时间窗口的序列神经网络（twSNN）。其中，VAE用于从原始传感器数据中提取隐藏和低维特征，损失函数用于提取有用的数据特征。通过使用滑动时间窗口，twSNN可以动态预测RUL；同时，它可以在时间维度上简化网络架构。此外，为了在各种故障条件下实现更高的性能，分别为twSNN设计了长短期记忆（LSTM）单元和卷积LSTM（ConvLSTM）单元。案例研究以飞机涡轮发动机的数据集完成。发现所提出的具有LSTM单元和ConvLSTM单元的框架在单故障模式和多故障模式上均具有更好的性能。结果还表明，单故障模式的预测准确度平均提高了6.65％，多故障模式的预测准确度平均提高了15.05％。