Papers by Author: Karen M. Holford

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 effect of lightning attachment to structures and vehicles is a cause of major concern to a number of different industries, in particular the aerospace industry, where the consequences of such an event can be catastrophic. In 1963, a Boeing 707 was brought down in Maryland killing 81 people on board, triggering the improvement of lightning protection standards. However, commercial jets are still struck on average once every 10,000 hours of flight time and between 1963 and 1989 forty lightning related accidents were recorded within the U.S.A alone. The rapid increase in the use of composite materials in aircraft design and the consequent increase in complexity when determining the effects of a lightning strike, has led to new challenges in aircraft protection and the requirement for improved understanding and standardisation.

Abstract: The detection of damage in gear teeth is paramount to any condition monitoring or structural health monitoring (SHM) tool for aerospace power transmissions such as those used in helicopters. Current inspection techniques include vibration analysis and time-inefficient visual inspection. Acoustic Emission (AE) is a very sensitive detection tool that has been successfully used in many SHM systems. Successful application of AE for damage detection in gear teeth will enable the optimisation of gear box design (and hence weight saving) in addition to safety improvements. This paper details a small aspect of a larger project designed to demonstrate automatic detection and location of common gear tooth defects. A novel test rig was designed to allow the fatigue loading of an individual gear tooth which was monitored using AE. The gear tooth was static in order to exclude the detection of AE signals arising from rotation; this allows initial development of the methodology prior to investigating rotating gears. Digital Image Correlation was used to determine the onset of cracking for comparison with the detected AE. Preliminary results of the investigation show that the developed methodology is appropriate for developing an automated gear health monitoring system and that future work should concentrate on the development of sensors and data acquisition methods associated with obtaining signals from rotating machinery.

Abstract: The use of structural health monitoring in the aerospace industry has many benefits including improved safety, reduced maintenance and extended aircraft lifecycles. A major focus of current research in this area is the development of wireless sensor 'nodes‘ which rely on batteries as a power source, severely limiting the product lifespan. This paper presents the results of work carried out to examine the feasibility of replacing or supplementing existing battery power supplies using thermoelectric energy conversion from ambient temperature differences in aircraft. An average power demand of 1mW over a typical sensor duty cycle is identified for current wireless sensor hardware. Temperature differentials between the wing fuel tanks and external air are determined and a theoretical model for thermoelectric energy harvesting potential is developed. Results indicate that average power outputs sufficient for the intended application of 6.6-22mW could be achieved during flight, based on a commercially available thermoelectric module of 30×30×4.1mm. An experimental investigation of the performance of this module when subjected to appropriate temperature conditions, using a Ranque-Hilshe vortex tube to generate easily controlled temperatures to -25°C is described. Excellent consistency is demonstrated between theoretical predictions and experimental results, confirming the accuracy of the theoretical model.

Abstract: The concept of harvesting energy is not a new one: there has been an interest in this area for around 10 years. Devices typically use either vibration (rigid body motion) or thermal gradients and can harvest sufficient energy to power telemetry, small devices or to charge a battery or capacitance device. However, for the new generation of aircraft, (both fixed wing and rotating) there is now an urgent need to develop energy harvesting systems in order to provide localised power for sensors in structural health monitoring systems (SHM). By implementing SHM, aircraft manufacturers can benefit from improved safety, reduced maintenance and extended aircraft life. The work presented examines the feasibility of designing an energy harvesting system powered by the vibrations of aircraft panels generated in flight. PZT (lead zirconate titanate) harvesters are bonded to an aluminium alloy panel, representative of an aircraft wing panel which is vibrated across a range of amplitudes (up to + 0.2mm) and frequencies (up to 300Hz). By recording voltage and current outputs from each harvester, generated power is calculated which when normalised for area and mass indicates values of up to 7.0 Wm-2 and 2.5Wkg-1 respectively, representing mechanical to electrical energy conversion efficiencies of up to 35% dependant on frequency of vibration. From these values it is estimated that a harvester area of down to 71cm2 or mass of as little as 20g is necessary to meet the current minimum power requirements of SHM systems of 50mW. With predicted reductions in sensor power consumption indicating system power requirements in the order of 0.1-1mW, this work shows that piezoelectric energy harvesting has future potential for powering aerospace SHM systems.

Abstract: Structural Health Monitoring (SHM) is of paramount importance in an increasing number of applications, not only to ensure safety and reliability, but also to reduce NDT costs and to ensure timely maintenance of critical components. This paper overviews the modern applications of acoustic emission (AE), which has become established as a very powerful technique for monitoring damage in a variety of structures, and the new approaches that have enabled the successful application of the technique, leading to automated crack detection. Examples are drawn from a variety of industries to provide an insight into the current role of AE in structural health monitoring.

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: This paper reports on a method for numerically modelling acoustic emission signals in simple plate geometries using dispersion curves. It is demonstrated how, by using a known source to sensor distance, it is possible to determine the arrival of the frequencies of the individual AE modes at the sensor face. Assumptions based on sensor frequency response and the amplitude of individual modes allow for an approximation of each mode arriving at the sensor face. These modes are then summed to provide a numerical model of the expected signal. Results of the model are compared with a recorded signal and show good correlation, this is further demonstrated by comparing the wavelet transforms of the modelled and recorded signal.

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.