In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

In recent years, the Remaining Useful Life (RUL) prediction has become a hot spot in Prognostics and Health Management (PHM) research. High-accuracy RUL prediction can reduce the probability of accident occurrence and improve the reliability of the mechanical system. This paper proposes a novel two-channel hybrid model for RUL prediction based on a Capsule Neural Network and a Long Short-Term Memory Network (Cap-LSTM). Conventional Convolutional Neural Networks (CNN) are widely used in RUL prediction. However, the pooling layer of the conventional CNN only extracts the most active part of the multivariate time-series sensor data. Moreover, conventional CNN is not sensitive to the direction and spatial position of features and thus the feature information may not be fully used. To overcome these shortcomings, this paper uses the capsule neural network to directly extract the highly correlated spatial feature information, from the multivariate time-series sensor data, thus avoiding the loss of the spatial position relationship between local features and reducing the complexity of the model. In order to obtain the training and test samples, this paper uses the sliding time window method to preprocess the data. Meanwhile, the piece-wise linear function is used to represent the actual performance degradation of the engine. The NASA C-MAPSS dataset is used to verify the prediction effectiveness of the Cap-LSTM model. The state-of-the-art RUL prediction models are also introduced and compared to the proposed approach, providing results that show the superiority of the presented method.