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.

The deep neural network is widely applied in remaining useful life (RUL) prediction because of its strong feature extraction ability. However, the prediction results of deep learning neural networks are often influenced by random noise and modeling parameters. Moreover, the training process of the traditional neural network is time-consuming. To overcome these drawbacks, a novel bootstrap ensemble learning convolutional simple recurrent unit (ELCSRU) method is proposed for RUL prediction. The simple recurrent unit is used to learn the time-series features of sensor data, which can effectively reduce the model parameters and boost the calculation speed. Moreover, the RUL prediction uncertainty can be quantified using the prediction interval, which can be calculated by the ELCSRU model. The prediction performance of the ELCSRU model is demonstrated using a turbofan engine dataset. The experimental results show that the proposed ELCSRU model provides a prognosis framework with better prediction performance for quantifying RUL prediction uncertainty.

In industrial production, effectively predicting the remaining useful life (RUL) of cutting tools can avoid overuse or underuse, which is of great significance for ensuring the processing quality of products and reducing enterprises’ production costs. This paper proposes a new method for RUL prediction of cutting tools based on robust empirical mode decomposition (REMD) and capsule bidirectional long short-term memory (Capsule-BiLSTM) network to improve accuracy. On one hand, new state features are extracted using REMD as the input of the deep learning network. On the other hand, a Capsule-BiLSTM network structure is designed to achieve RUL prediction of cutting tools by connecting the four layers. Finally, the effectiveness of the proposed method is verified by a series of cutting tool life tests. Comparison with some mainstream methods indicates that the proposed method has more advantages in RUL prediction of cutting tools with the average accuracy reaching up to 93.97%.

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.