A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview

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Abstract

The acoustic emission (AE) phenomenon has many attributes that make it desirable as a structural health monitoring or non-destructive testing technique, including the capability to continuously and globally monitor large structures using a sparse sensor array and with no dependency on defect size. However, AE monitoring is yet to fulfil its true potential, due mainly to limitations in location accuracy and signal characterisation that often arise in complex structures with high levels of background noise. Furthermore, the technique has been criticised for a lack of quantitative results and the large amount of operator interpretation required during data analysis. This paper begins by introducing the challenges faced in developing an AE based structural health monitoring system and then gives a review of previous progress made in addresing these challenges. Subsequently an overview of a novel methodology for automatic detection of fatigue fractures in complex geometries and noisy environments is presented, which combines a number of signal processing techniques to address the current limitations of AE monitoring. The technique was developed for monitoring metallic landing gear components during pre-flight certification testing and results are presented from a full-scale steel landing gear component undergoing fatigue loading. Fracture onset was successfully identify automatically at 49,000 fatigue cycles prior to final failure (validated by the use of dye penetrant inspection) and the fracture position was located to within 10 mm of the actual location.

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Holford, K. M., Eaton, M. J., Hensman, J. J., Pullin, R., Evans, S. L., Dervilis, N., & Worden, K. (2017, April 1). A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview. Progress in Aerospace Sciences. Elsevier Ltd. https://doi.org/10.1016/j.paerosci.2016.11.003

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