A review of fault management issues in aircraft systems: Current status and future directions

Zolghadri, A.

doi:10.1016/j.paerosci.2024.101008

Progress in Aerospace Sciences

2024

DOI: 10.1016/j.paerosci.2024.101008

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A review of fault management issues in aircraft systems: Current status and future directions

A. Zolghadri

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2024

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Cited by 2 publications

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A Fault Diagnosis Method for Avionics Equipment Based on SMOTEWB‐LGBM

Li,

Wen

et al. 2024

International Journal of Aerospace Engineering

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To tackle the common issue of imbalanced data classes in the fault diagnosis of avionics equipment, this study proposes a method that integrates the Synthetic Minority Oversampling Technique (SMOTE) with Boosting (SMOTEWB) and the Light Gradient Boosting Machine (LGBM). Initially, SMOTEWB is refined through a “successive one‐vs‐many balancing strategy,” which effectively addresses multiclass sample balance issues. The enhanced data is then employed for pattern recognition and classification using LGBM. Additionally, this study utilizes the Tree‐structured Parzen Estimator (TPE) method and five‐fold cross‐validation to optimize the model’s hyperparameters, thus improving diagnostic accuracy. Experimental validation using University of California Irvine (UCI) public datasets and real‐world avionics equipment fault data shows that the proposed SMOTEWB‐LGBM method outperforms other common methods in handling multiclass imbalanced datasets. This new approach not only enhances fault diagnosis efficiency but also offers a potent solution for similar multiclass imbalance challenges.

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A Fault Diagnosis Method for Avionics Equipment Based on SMOTEWB‐LGBM

Li,

Wen

et al. 2024

International Journal of Aerospace Engineering

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A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

Yuan,

Adjallah,

Sava

et al. 2024

Sensors

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The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

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A review of fault management issues in aircraft systems: Current status and future directions

Cited by 2 publications

References 110 publications

A Fault Diagnosis Method for Avionics Equipment Based on SMOTEWB‐LGBM

A Fault Diagnosis Method for Avionics Equipment Based on SMOTEWB‐LGBM

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

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