An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing

Dong, Yiqun

doi:10.1016/j.ast.2018.02.026

Cited by 60 publications

(17 citation statements)

References 17 publications

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“…A Convolutional Neural Network (CNN) and a Recursive Neural Network with Long Short-Term Memory (RNN (LTSM)) layers were implemented in parallel to output vectors that were subsequently merged and processed in fully connected layers. Inputting data to different branches to allow separate extraction of the time dependent and the current parameter values was expected to lead to improved accuracy when compared to sequential treatment by CNN and LSTM layers [7].…”

Section: Machine Learningmentioning

confidence: 99%

LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process

McCormick

Villa

2019

Lecture Notes in Computer Science

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Anaerobic digestion is a natural process that transforms organic substrates to methane and other products. Under controlled conditions the process has been widely applied to manage organic wastes. Improvements in process control are expected to lead to improvements in the technical and economic efficiency of the process. This paper presents and compares 3 different neural network model architectures for use as anaerobic digestion process predictive models. The models predict the future biogas production trend from measured physical and chemical parameters. The first model features an LSTM layer, the second model features a 1-D convolutional layer and the third model combines 2 separate inputs and parallel treatment using LSTM and 1-D convolutional layers followed by merging to produce a single prediction. The predictions can be used to adaptively adjust the substrate feeding rate in accordance with the transient state of the digestion process as defined by liquid feeding rate, the organic acid and ammonium ion concentrations and the pH of the digester liquid phase. The training and testing data were obtained during 1 year of continuous operation of a pilot-plant treating restaurant wastes. PLS regression and ICA were used to select the most relevant process parameters from the data. The 1-D Convolutional based model comprising 272 trainable parameters predicted the future biogas flow rate changes with accuracy as high as 89% and an average accuracy of 58%. The work-flow can be applied to optimize the control of the study digester and to control bioreactors in general.

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Section: Machine Learningmentioning

confidence: 99%

LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process

McCormick

Villa

2019

Lecture Notes in Computer Science

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“…In view of the advantages of high computational efficiency and high accuracy, ANN has been widely used in data mining and pattern recognition [20,21]. In the field of aviation, Dong applies the deep neural network to aircraft parameter identification to detect and characterize aircraft icing [22]; Omar Alkhamisi and Mehmood used the integration of the machine learning algorithm and deep learning algorithm to improve risk prediction in the aviation system [23]; Zhang and Mahadevan trained two different types of deep learning models from different angles to predict flight path and proposed a risk prediction method based on the deep learning longterm short-term memory structure recurrent neural network [24]. It can be seen that ANN has strong nonlinear mapping ability and simplification ability and can learn historical data to quantitatively predict the trend of selected parameters.…”

Section: Introductionmentioning

confidence: 99%

Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

Liu

Feng

et al. 2021

Shock and Vibration

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In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design.

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“…Many scholars have applied deep learning in the field of civil aviation and achieved remarkable success. Dong applied deep neural networks to aircraft parameter identification to detect and characterize aircraft icing conditions [23]. Omar Alkhamisi and Mehmood used the integration of machine learning algorithm and deep learning algorithm to improve risk prediction in aviation system [24].…”

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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An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing

Cited by 60 publications

References 17 publications

LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process

LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process

Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

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

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