A recurrent neural network based method for predicting the state of aircraft air conditioning system

Zhang, Yuxuan; Li, Yuanxiang; Wei, Xian; Peng, Xishuai; Zhao, Hongyan; Shen, Kaijie

doi:10.1109/ssci.2017.8285339

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…To verify that the proposed characteristic prediction module was effective and efficient, it was compared with the RNN [ 38 ], LSTM [ 39 ], GRU [ 40 ], and BiLSTM networks in terms of both real time and mean square error, and a segment of the predicted trajectory with the characteristic of the distance between the two enemy sides was selected for comparison with the actual trajectory. The results are shown in Table 1 and Figure 10 .…”

Section: Experimental Analysismentioning

confidence: 99%

A GRU‐Based Method for Predicting Intention of Aerial Targets

Teng

Song

Gang

et al. 2021

Computational Intelligence and Neuroscience

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Since a target’s operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific nor effective enough. Based on a gated recurrent unit (GRU), a bidirectional propagation mechanism and attention mechanism are introduced in a proposed aerial target combat intention recognition method. The proposed method constructs an air combat intention characteristic set through a hierarchical approach, encodes into numeric time-series characteristics, and encapsulates domain expert knowledge and experience in labels. It uses a bidirectional gated recurrent units (BiGRU) network for deep learning of air combat characteristics and adaptively assigns characteristic weights using an attention mechanism to improve the accuracy of aerial target combat intention recognition. In order to further shorten the time for intention recognition and with a certain predictive effect, an air combat characteristic prediction module is introduced before intention recognition to establish the mapping relationship between predicted characteristics and combat intention types. Simulation experiments show that the proposed model can predict enemy aerial target combat intention one sampling point ahead of time based on 89.7% intent recognition accuracy, which has reference value and theoretical significance for assisting decision-making in real-time intention recognition.

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Section: Experimental Analysismentioning

confidence: 99%

A GRU‐Based Method for Predicting Intention of Aerial Targets

Teng

Song

Gang

et al. 2021

Computational Intelligence and Neuroscience

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“…In this paper, a hazard prediction method based on deep learning of long-short-term memory structure recurrent neural network (RNN) is proposed when the identification model judges that there is no hazard in the system. RNN has been successfully applied to various sequence prediction [26]- [28]. Unfortunately, RNN is a very deep feed forward network in which all the layers share the same weights.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approach for Civil Aircraft Hazard Identification and Prediction

et al. 2020

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Safety is an eternal issue in the civil aviation transportation. Once a civil aviation accident occurs, it will cause great casualties and economic losses. In order to ensure the civil aviation safety, the hazard identification and prediction of civil aircraft should be effectively and accurately realized. The civil aircraft uses Aircraft Communications Addressing and Reporting System (ACARS) to interact with the ground during flight. The data generated by ACARS has a simple structure and strong timeliness. In view of the advantages of ACARS data, a hazard identification and prediction method based on support vector machine optimized by particle swarm optimization (PSO-SVM) and long short term memory (LSTM) neural networks which uses ACARS report as analysis data is proposed. First, in order to reduce the identification and prediction time cost, the SVM-based recursive feature elimination method with cross-validation algorithm (SVM-RFECV) is used to select the characteristic parameters. Then, the SVM optimized by PSO is used to identify hazard based on the selected parameters. According to the identification results, the LSTM is used to predict the trend of the selected parameters to realize hazard prediction. An A13 report of APU generated by ACARS is selected as analysis data for hazard identification and predication in this paper. The analysis results show that the proposed identification method based on PSO-SVM and SVM-RFECV has high identification speed and accuracy. The proposed prediction method based on LSTM has the best prediction performance. The proposed method can effectively identify hazards and accurately predict the trend of parameters to improve the safety of aircraft. INDEX TERMS Aviation safety, hazard identification, hazard prediction, deep learning, long short term memory (LSTM).

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Preventive Maintenance Strategy for Air Conditioning Systems Based on Health Index Prediction

Jin,

Xu,

2024

2024 IEEE 7th International Electrical and Energy Conference (CIEEC)

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A recurrent neural network based method for predicting the state of aircraft air conditioning system

Cited by 4 publications

References 13 publications

A GRU‐Based Method for Predicting Intention of Aerial Targets

A GRU‐Based Method for Predicting Intention of Aerial Targets

Deep Learning-Based Approach for Civil Aircraft Hazard Identification and Prediction

Preventive Maintenance Strategy for Air Conditioning Systems Based on Health Index Prediction

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