Papers by Author: James J. Hensman

Abstract: Acoustic Emission (AE) is a passive form of non-destructive testing that relies on the detection and analysis of stress waves released during crack propagation. AE techniques are successfully employed number of industries there remains some scepticism in aerospace engineering. The reported investigation details a single four point bend test specimen undergoing fatigue loading. This test is part of a much larger programme designed to demonstrate a technology readiness level (TRL) of five of the use of AE to detect crack initiation and growth in landing gear structures. The completed test required that crack growth had to be monitored to allow a comparison with the detected and located AE signals. The method of crack monitoring had to be non-contact so as not to produce frictional sources of AE in the crack region, preventing the use of crack mouth opening displacement gauges. Furthermore adhesives on the specimen surface had to be avoided to eliminate the possibility that the detected AE was from adhesive cracking, thus the use of strain gauges or foil crack gauges was not possible. A method using Digital Image Correlation (DIC) to monitor crack growth was investigated. The test was stopped during fatigue loading at 1000 cycle intervals and a DIC image captured at peak load. The displacement due to crack growth was observed throughout the investigation and the results compared with the detected AE signals. Results showed a clear correlation between AE and crack growth and added further evidence of TRL5 for detecting fractures in landing gears using AE.

Abstract: The optimal selection of discriminatory features from large datasets remains a pressing problem in damage identification. In this paper, a Bayesian approach to classification and feature selection is introduced and applied to a challenging experimental problem.

Abstract: This paper details progress towards the application of a methodology for Acoustic Emission (AE) detection and interpretation for the monitoring of fatigue fractures in large-scale industrial environments. An artificial acoustic emission source, representative of a fatigue fracture was injected into a test of a substantial landing gear component. An AE monitoring system was then used to successfully locate and identify the source using the new signal processing methodology.

Abstract: This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

Abstract: Acoustic emission monitoring was completed on a painted aerospace grade steel landing gear component undergoing fatigue loading until rupture. A post-test linear location analysis of the collected signals revealed eleven groups where high activity (greater than 2000 hits) occurred within a defined location, three of which corresponded in location to the position of fracture and final rupture of the specimen. Feature data, such as amplitude, rise-time, energy etc. were used to describe the identified signals in each group. A dimension reduction through principal component analysis of the feature data of all groups was performed. Results showed that high amplitude signals associated with four groups of signals arising from noise could be separated from the fracture groups. However four groups not associated with noise or the known positions of the fracture groups were not separable from the signals attributed to fractures. The paint layer of the specimen was removed and a magnetic particle investigation was completed that showed these four groups coincided with regions of additional fracture in the component.

Abstract: An acoustic emission test was simulated using a three point bend specimen and an artificial AE source. Waveform data was recorded as the sample was cyclically loaded in three point bending, and the cross correlation coefficient of the waveforms was used to measure the repeatability of the test. Results were twofold: the stress state of a specimen affects the ultrasonic propagation therein; and the coupling condition of a transducer may not remain constant during a test.