Fusion and comparison of prognostic models for remaining useful life of aircraft systems

Changes in the performance of an aircraft system will straightforwardly affect the safe operation of the aircraft, and the technical requirements of Prognostics and Health Management (PHM) are highly relevant. Remaining Useful Life (RUL) prediction, part of the core technologies of PHM, is a cutting-edge innovation being worked on lately and an effective means to advance the change of upkeep support mode and work on the framework's security, unwavering quality, and economic reasonableness. This paper summarizes a detailed preliminary literature review and comparison of different prognostic approaches and the forecasting methods' taxonomy, the methodology's details, and provides its application to aircraft systems. It also provides a brief introduction to the predictive maintenance concept and condition-based maintenance (CBM). This article uses several predictive models to predict RUL and classifies conventional regression algorithms according to the similarity in function and form of the algorithms. More classical algorithms in each category are selected to compare the prediction results, and finally, the combined effects of the RUL prediction are obtained by weighted fusion, accuracy, and compatibility. The performance of the proposed models is assessed based on evaluations of RUL acquired from the hybrid and individual predictive models. This correlation depends on the most current prognostic metrics. The outcomes show that the proposed strategy develops precision, robustness, and adaptability. Hence, the work in this paper shall enrich the advancement of predictive maintenance and modern innovation of prognostic development

CNN-fusion architecture with visual and thermographic images for object detection

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

Mooring Chain Climbing Robot For Ndt Inspection Applications

Inspection of mooring chains is a dangerous and costly procedure covering inspection above and below the waterline. The paper presents initial results from the RIMCAW project which was aimed at designing and building an inspection robot able to climb mooring chains and deploy NDT technologies for scanning individual links thereby to detecting critical defects. The paper focuses on the design and realisation of the inch worm type novel crawler developed and tested in the TWI Middlesbrough water tank

Mooring Chain Climbing Robot For Ndt Inspection Applications

Inspection of mooring chains is a dangerous and costly procedure covering inspection above and below the waterline. The paper presents initial results from the RIMCAW project which was aimed at designing and building an inspection robot able to climb mooring chains and deploy NDT technologies for scanning individual links thereby to detecting critical defects. The paper focuses on the design and realisation of the inch worm type novel crawler developed and tested in the TWI Middlesbrough water tank