ArticleSensors (Basel, Switzerland)2020
A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Vanilla LSTM Predictive Maintenance Model for Scientific Research Facilities.Sensors (Basel, Switzerland) · 2026Article
- Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management.Sensors (Basel, Switzerland) · 2024Article
- The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.Sensors (Basel, Switzerland) · 2024Article
- A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory.Sensors (Basel, Switzerland) · 2024Article
- Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Adaptively Lightweight Spatiotemporal Information-Extraction-Operator-Based DL Method for Aero-Engine RUL Prediction.Sensors (Basel, Switzerland) · 2023Article
- Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction.Sensors (Basel, Switzerland) · 2023Article
- A Novel Preoperative Prediction Model Based on Deep Learning to Predict Neoplasm T Staging and Grading in Patients with Upper Tract Urothelial Carcinoma.Journal of clinical medicine · 2022Article
- EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction.BMC bioinformatics · 2022Article
- Human Activity Recognition Based on Residual Network and BiLSTM.Sensors (Basel, Switzerland) · 2022Article
- Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management.Sensors (Basel, Switzerland) · 2021Article
- Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.