Towards an Intelligent Decision Support System for Aircraft Troubleshooting

Yurin, Aleksandr Yu.; Kotlov, Yuri V.; Popov, Vladimir M.; Mishin, Sergey

doi:10.1007/978-981-19-3788-0_7

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Models for predicting tires replacement were introduced by [30] and [31]. Structural health management was proposed by [32], defect prediction by means of digital twins was investigated by [33], troubleshooting was studied by [34], neural networks together with AI and ML were used by [35] and [36] to forecast lumpy demand.…”

Section: Category 3: Process Development In Maintenance Repair and Ov...mentioning

confidence: 99%

Technology Roadmap for Aircraft Maintenance, Repair and Overhaul

Ichou

Veress

2022

RepTudKoz

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Nowadays, the demand for aircraft Maintenance, Repair and Overhaul (MRO) is constantly growing. The market size of the European MRO segment is estimated to be USD 206.13 billion in 2022, growing at a Compound Annual Growth Rate (CAGR) of 2.8% between 2022 and 2030 [1]. This forecast is a good indication of the growth in the number of incoming assignments. As a result, airlines and aircraft operators will increasingly rely on companies with experience in the MRO field to perform maintenance and repair work. Furthermore, as many airlines now choose to outsource maintenance and repair, this will further increase the load on MRO companies. As the number of incoming jobs increases, the companies concerned are constantly looking for and implementing new and better methods and technologies, with another aim of gaining a larger market share. Moreover, as there is still scope for the development and introduction of new technologies and processes in this area, a significant number of research and development projects are underway or in the pipeline. Therefore, the main objective of this study is to use the available information to present a generalised technology roadmap for the companies involved in MRO activities and, on this basis, to collect, present and categorise the state-of-the-art developments in the MRO sector, highlighting what the future will hold for companies that incorporate these revolutionary innovations into their daily work processes.

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Section: Category 3: Process Development In Maintenance Repair and Ov...mentioning

confidence: 99%

Technology Roadmap for Aircraft Maintenance, Repair and Overhaul

Ichou

Veress

2022

RepTudKoz

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“…Lili Huang utilized Quick Access Recorder (QAR) data for scenario replication and troubleshooting in civil aviation safety management [13]. Furthermore, attempts to employ AI methods have been made, as demonstrated by Aleksandr Yurin et al, trying to achieve automation and intelligence in aircraft troubleshooting [14]. In summary, the methodological foundation of existing relevant research exhibits a trend diverging from "model-based" approaches towards some innovative directions (Table 2), with big data and artificial intelligence being two representative methodologies.…”

Section: Introductionmentioning

confidence: 99%

Onboard remote high-definition collaborative troubleshooting and flight decision-making assistance device

Shao,

Song,

Guo

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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In the process of aircraft design, various experiments are required to validate design parameters under real flight conditions. Such experiments are costly, and currently there are limited solutions for effectively dealing with scenarios such as occasional faults during these experiments. This paper proposes an on-board remote high-definition collaborative troubleshooting and flight decision-making assistance device, aiming to achieve efficient test scenario replication and decision support through remote collaboration between ground personnel and flight crew. The device utilizes several image capture devices in the cockpit and employs algorithms such as action recognition and image recognition. It utilizes highspeed satellite communication technology to structure flight data, including pilot operations, faults, and aircraft status, into a timeline script. Ground personnel can use this output script to provide corresponding solutions. Flight crew members receive real-time guidance and decision support through smart glasses. It is evaluated that this approach may save a significant amount of travel expenses and time in ground tests, flight tests, field fault diagnosis, and crew training scenarios. The innovation of the proposed device lies in the application of cloud servers for timeline script generation and the use of remote retrieval by monitoring personnel for key operation fault diagnosis. Furthermore, it incorporates intelligent recognition technology for flight guidance, enabling efficient troubleshooting and reducing the number of flight tests. Therefore, research and development costs could be reduced, and engineering change progress could be accelerated.

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