| A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification |
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| 作者姓名： | Di ZHOU Xiao ZHUANG Hongfu ZUO |
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| 作者单位： | 1. Civil Aviation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance, College of Civil Aviation,Nanjing University of Aeronautics and Astronautics;2. Department of Mechanical and Industrial Engineering, University of Toronto;3. College of Science, Nanjing University of Aeronautics and Astronautics |
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| 基金项目： | co-supported by National Natural Science Foundation of China (No. U1933202)；;China Scholarship Council (CSC) (No. 201906830043)；;Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Nos. KYCX18＿0310 and KYCX18＿0265)； |
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| 摘 要： | Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term M...
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| 收稿时间： | 17 November 2020 |
A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification |
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| Di ZHOU,Xiao ZHUANG,Hongfu ZUO.A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification[J].Chinese Journal of Aeronautics,2022,35(4):344-361. |
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| Institution: | 1. Civil Aviation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada;3. College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit (APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So, actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network (HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-BiLSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-BiLSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples. |
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| Keywords: | Civil aviation Convolutional neural networks Deep neural networks Hazard identification Long short-term memory |
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