Aircraft wheels and brakes are safety critical systems which play a critical role in supporting aircraft ground operations, specifically during the aircraft deceleration phase. The energy absorbed by the wheels and brakes has a significant impact on the nitrogen pressure within the tyre, which can potentially impact the tyre performance. This could lead to: unscheduled maintenance on wheels and brakes, and/or fuel inefficiency, and/or increase in overall maintenance costs, and/or accidents during ground operation. The existing single aisle commercial aircraft have insufficient instrumentation on wheels and brakes and therefore provide limited technical data to support the understanding of wheel and brake temperatures and their interdependencies with the external environment. In this paper an innovative approach has been proposed, and demonstrated, to capture the temperature signatures of various critical locations on the aircraft wheels and brakes based on the use of an infrared thermographic camera. To support the overall implementation of this research study, the Cranfield University National Flying Laboratory Centre (NFLC) Saab 340B (Registration number G-NFLB) aircraft has been investigated. The wheel and brake temperature signatures have been acquired corresponding to two different flight profiles. The acquired results suggest that the employed thermographic camera can consistently capture temperature trends at all target locations across both flight profiles. Furthermore, trends detected at each location provide engineering insight into the cooling pattern corresponding to each location, including the influence of the external environment. The results therefore pave the scientific foundation to further develop the engineering understanding of the wheel and brake temperature data set that can be utilised in supporting the implementation of a condition monitoring solution for accurate prediction of tyre pressure.
Mobile robots performing aircraft visual inspection play a vital role in the future automated aircraft maintenance, repair and overhaul (MRO) operations. Autonomous navigation requires understanding the surroundings to automate and enhance the visual inspection process. The current state of neural network (NN) based obstacle detection and collision avoidance techniques are suitable for well-structured objects. However, their ability to distinguish between solid obstacles and low-density moving objects is limited, and their performance degrades in low-light scenarios. Thermal images can be used to complement the low-light visual image limitations in many applications, including inspections. This work proposes a Convolutional Neural Network (CNN) fusion architecture that enables the adaptive fusion of visual and thermographic images. The aim is to enhance autonomous robotic systems’ perception and collision avoidance in dynamic environments. The model has been tested with RGB and thermographic images acquired in Cranfield’s University hangar, which hosts a Boeing 737-400 and TUI hangar. The experimental results prove that the fusion-based CNN framework increases object detection accuracy compared to conventional models.
Infrared thermography is a condition monitoring technique that, from a measurement of the radiant heat pattern emitted by a material, is able to determine regions or points of increased or reduced heat emission that can indicate the presence of an imperfection in the investigated material. The result of an infrared thermographic investigation is a sequence of thermograms or thermal images, in other words a picture of temperature, that can be further processed for qualitative and quantitative purposes. Such images can be presented in either false color or black and white format. In the present work, the philosophy and history of thermal–infrared imaging are reviewed. Moreover, the different evaluation approaches (passive and active), as well as many standards related to infrared thermography are discussed. Finally, various applications of the transient thermography approach are briefly presented
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