Recherchiere Firmenbekanntmachungen und finanzielle Kennzahlen
UK-Förderung (915.848 £): Künstlich intelligentes Überwachungssystem für Flughafen-Gepäckabfertigungsanlagen (AIMS) Ukri01.11.2019 Forschung und Innovation im Vereinigten Königreich, Großbritannien
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Künstlich intelligentes Überwachungssystem für Flughafen-Gepäckabfertigungsanlagen (AIMS)
| Zusammenfassung | Mishandled bags create misery for passengers, are expensive for the airline industry (estimated at £1.76b p.a. in 2017), and damaging to reputations. An estimated 1.6 per 1000 bags are mishandled at Heathrow due to faults with the baggage handling equipment. 140k passengers per day and compensation of ~£100 per bag equates to airlines losing £22,400 a day. In response, Heathrow has set an objective of "Every passenger, every bag, every time" and baggage systems down-time of less than 1/2 hour. Unfortunately, most of the common failures, typically of a motor gearbox unit (MGU) or conveyor, take more than an hour to repair. The only option is to carry out maintenance during the nightly shut-down. This is not usually possible either, because there is no advance warning of equipment faults before they are serious enough to require un-planned shutdowns. Sophisticated condition monitoring systems exist, but they are only used for high capital value, critical assets because they are too expensive to deploy on a large scale (~30,000 MGUs at Heathrow). One of the best systems is vibration monitoring, because it provides a wealth of information on damage to critical machine parts such as gears, bearings and electric motors as well as faults such as misalignment, imbalance, and cracks. High quality accelerometers required for predictive vibration monitoring cost ~£500, which is untenable for widespread deployment at an airport. Unfortunately, low cost accelerometers do not perform well enough to detect early stage damage. Motor current signature analysis (MCSA) could be an effective, low cost alternative. Currently, it also suffers from poor diagnosis capability due to limitations of present signal processing algorithms for motor current data. AIMS will overcome this challenge by developing: a) Novel signal processing to extract high fidelity diagnostic features for early stage damage and faults to gears, conveyors, bearings, motors and shafts from motor current data using low cost current clamps. b) A novel artificial intelligence that is able to classify the health of MGUs/Conveyors. The output of this system will be used to create an easy to interpret traffic light display of baggage handling asset health and prioritised maintenance actions for the engineers. The system will have widespread application in airports and industries involving mechanical handling such as fast moving consumer goods (FMCG) and warehousing. AIMS will unlock the opportunity to generate (discounted) gross profits of £21.9m in a 5 year post project period. 26/04/21 - update to scope to change project trials from Heathrow to Schipol airport |
| Kategorie | Collaborative R&D |
| Referenz | 35195 |
| Status | Active |
| Laufzeit von | 01.11.2019 |
| Laufzeit bis | 31.01.2023 |
| Fördersumme | 915.848,00 £ |
| Quelle | https://gtr.ukri.org/projects?ref=35195 |
Beteiligte Organisationen
| Asset Handling Limited |
304.390,00 £ |
| University of Huddersfield |
495.875,00 £ |
| Babcock Airports Limited |
115.583,00 £ |
Die Bekanntmachung bezieht sich auf einen vergangenen Zeitpunkt, und spiegelt nicht notwendigerweise den heutigen Stand wider. Der aktuelle Stand wird auf folgender Seite wiedergegeben: Asset Handling Ltd., Wirral, Großbritannien.
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