The Study of Aircraft Accidents Causes by Computer Simulations

Szczepaniak, Paweł; Jastrzębski, Grzegorz; Sibilski, Krzysztof; Bartosiewicz, Andrzej

doi:10.3390/aerospace7040041

Cited by 5 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a fast, economical, safe, and efficient research approach, simulation modelling has been used in the aviation industry for various purposes, including aircraft design [44], optimization [45], manufacturing [46], air traffic management [47], pilot training [48], accident investigation [49], and MRO (Maintenance, Repair and Overhaul) [50]. For the fuel system, simulation can assist the system's design, verification, and optimization processes.…”

Section: Fuel System Simulationmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

View full text Add to dashboard Cite

The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.

show abstract

Section: Fuel System Simulationmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

View full text Add to dashboard Cite

show abstract

“…There are three problems still exist: (1) In the current research on emotion or mental classification, the proposed classification models are relatively complex and generally have poor generalization. (2) In flight, the pilot needs to obtain a large amount of information, coupled with the influence of various noises, the collected EEG signal contains a lot of useless information, and it is very difficult to extract effective information with high accuracy.…”

Section: Motivationmentioning

confidence: 99%

“…With the continuous improvement of aircraft automation, the proportion of flight accidents caused by equipment failure has dropped from 85% in the middle of the 20th century to 12%-21% now [1,2]. The mental state of pilots has become an essential factor in affecting flight safety [3].…”

Section: Introductionmentioning

confidence: 99%

A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

Wang¹,

Wang²,

Xiong³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

How to accurately recognize the mental state of pilots is a focus in civil aviation safety. The mental state of pilots is closely related to their cognitive ability in piloting. Whether the cognitive ability meets the standard is related to flight safety. However, the pilot's working state is unique, which increases the difficulty of analyzing the pilot's mental state. In this work, we proposed a Convolutional Neural Network (CNN) that merges attention to classify the mental state of pilots through electroencephalography (EEG). Considering the individual differences in EEG, semi-supervised learning based on improved K-Means is used in the model training to improve the generalization ability of the model. We collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this data. The method in this paper achieved an accuracy of 86.29%, which is better than 4D-aNN and HCNN etc. Negative emotion will increase the probability of fatigue appearing, and emotion recognition is also meaningful during the flight. Then we conducted experiments on the public dataset SEED, and our method achieved an accuracy of 93.68%. In addition, we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.

show abstract

“…Aircraft design defects, including design errors, functional errors, or logic errors, will lead to airplane upsets or loss of control of aircraft in a specific situation [40].…”

Section: Aircraft Design Defects (M 2 )mentioning

confidence: 99%

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Meng

2023

Aerospace

View full text Add to dashboard Cite

Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and coupling mechanism of airplane upsets, this study proposed a risk analysis model integrating the Interpretative Structural Modeling (ISM) and Bayesian Network (BN). Seventeen key risk factors leading to airplane upsets were identified through the analysis of typical accident cases and the literature. The ISM approach was used to construct the multi-level interpretative structural model of airplane upsets, which could reveal the causal relationship among various risk factors and risk propagation paths. Then, taking 286 accident/incident investigation data as training samples, a data-driven BN model was established using machine learning for dependency intensity assessment and inference analysis. The results reveal that the interaction among risk factors of fatal accidents caused by airplane upsets is more significant than that of non-fatal accidents/incidents. Risk factors such as pilot-induced oscillations/airplane-pilot coupling and non-adherence to Standard Operating Procedures (SOPs)/neglect of cross-validation have a significant effect on airplane upsets in flight among seventeen risk factors. Moreover, this study also identifies the most likely set of risk factors that lead to fatal accidents caused by airplane upsets. The research results have an important theoretical significance and application value for preventing airplane upsets risk.

show abstract

The Study of Aircraft Accidents Causes by Computer Simulations

Cited by 5 publications

References 15 publications

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Contact Info

Product

Resources

About