A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved t...
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MDPI AG
2025-02-01
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| author | Jinyin Bai Wei Zhu Shuhong Liu Chenhao Ye Peng Zheng Xiangchen Wang |
| author_facet | Jinyin Bai Wei Zhu Shuhong Liu Chenhao Ye Peng Zheng Xiangchen Wang |
| author_sort | Jinyin Bai |
| collection | DOAJ |
| description | Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction. |
| format | Article |
| id | doaj-art-fa0ad8d3e24142ceb7ae656f2a5b2345 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-fa0ad8d3e24142ceb7ae656f2a5b23452025-08-20T02:44:36ZengMDPI AGApplied Sciences2076-34172025-02-01154170210.3390/app15041702A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial EquipmentJinyin Bai0Wei Zhu1Shuhong Liu2Chenhao Ye3Peng Zheng4Xiangchen Wang5School of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaTraditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction.https://www.mdpi.com/2076-3417/15/4/1702time-series faultTCNBiLSTMfault predictionhyperparameter optimization |
| spellingShingle | Jinyin Bai Wei Zhu Shuhong Liu Chenhao Ye Peng Zheng Xiangchen Wang A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment Applied Sciences time-series fault TCN BiLSTM fault prediction hyperparameter optimization |
| title | A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment |
| title_full | A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment |
| title_fullStr | A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment |
| title_full_unstemmed | A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment |
| title_short | A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment |
| title_sort | temporal convolutional network bidirectional long short term memory tcn bilstm prediction model for temporal faults in industrial equipment |
| topic | time-series fault TCN BiLSTM fault prediction hyperparameter optimization |
| url | https://www.mdpi.com/2076-3417/15/4/1702 |
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