Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Thermally evaporated tungsten oxide (WO3) thin films for gas sensing applications

In this thesis, the author proposed and developed gas sensors made of nanostructured WO3 thin film by a thermal evaporation technique. This technique gives control over film thickness, grain size and purity. The device fabrication, nanostructured material synthesis, characterization and gas sensing performance have been undertaken. Three different types of nanostructured thin films, namely, pure WO3 thin films, iron-doped WO3 thin films by co-evaporation and Fe-implanted WO3 thin films have been synthesized. All the thin films have a film thickness of 300 nm. The physical, chemical and electronic properties of these films have been optimized by annealing heat treatment at 300ºC and 400ºC for 2 hours in air. Various analytical techniques were employed to characterize these films. Atomic Force Microscopy and Transmission Electron Microscopy revealed a very small grain size of the order 5-10 nm in as-deposited WO3 films, and annealing at 300ºC or 400ºC did not result in any significant change in grain size. X-ray diffraction (XRD) analysis revealed a highly amorphous structure of as-deposited films. Annealing at 300ºC for 2 hours in air did not improve crystallinity in these films. However, annealing at 400ºC for 2 hours in air significantly improved the crystallinity in pure and iron-doped WO3 thin films, whereas it only slightly improved the crystallinity of iron-implanted WO3 thin film as a result of implantation. Rutherford backscattered spectroscopy revealed an iron content of 0.5 at.% and 5.5 at.% in iron-doped and iron-implanted WO3 thin films, respectively. The RBS results have been confirmed using energy dispersive x-ray spectroscopy (EDX) during analysis of the films using transmission electron microscopy (TEM). X-ray photoelectron spectroscopy (XPS) revealed significant lowering of W 4f7/2 binding energy in all films annealed at 400ºC as compared with the as-deposited and 300ºC annealed films. Lowering of W 4f7/2 is due to increase in number of oxygen vacancies in the films and is considered highly beneficial for gas sensing. Raman analysis revealed that 400ºC annealed films except the iron-implanted film are highly crystalline with significant number of O-W-O bonds, which was consistent with the XRD results. Additionally, XRD, XPS and Raman analyses showed no evidence of secondary peaks corresponding to compounds of iron due to iron doping or implantation. This provided an understanding that iron was incorporated in the host WO3 matrix rather than as a separate dispersed compound or as catalyst on the surface. WO3 thin film based gas sensors are known to operate efficiently in the temperature range 200ºC-500 ºC. In the present study, by optimizing the physical, chemical and electronic properties through heat treatment and doping, an optimum response to H2, ethanol and CO has been achieved at a low operating temperature of 150ºC. Pure WO3 thin film annealed at 400ºC showed the highest sensitivity towards H2 at 150ºC due to its very small grain size and porosity, coupled with high number of oxygen vacancies, whereas Fe-doped WO3 film annealed at 400ºC showed the highest sensitivity to ethanol at an operating temperature of 150ºC due to its crystallinity, increased number of oxygen vacancies and higher degree of crystal distortions attributed to Fe addition. Pure WO3 films are known to be insensitive to CO, but iron-doped WO3 thin film annealed at 300ºC and 400ºC showed an optimum response to CO at an operating temperature of 150ºC. This result is attributed to lattice distortions produced in WO3 host matrix as a result of iron incorporation as substitutional impurity. However, iron-implanted WO3 thin films did not show any promising response towards the tested gases as the film structure has been damaged due to implantation, and annealing at 300ºC or 400ºC was not sufficient to induce crystallinity in these films. This study has demonstrated enhanced sensing properties of WO3 thin film sensors towards CO at lower operating temperature, which was achieved by optimizing the physical, chemical and electronic properties of the WO3 film through Fe doping and annealing. This study can be further extended to systematically investigate the effects of different Fe concentrations (0.5 at.% to 10 at.%) on the sensing performance of WO3 thin film gas sensors towards CO.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

The calculation of acoustic indices to characterise acoustic recordings of the environment

This technical report describes the methods used to obtain a list of acoustic indices that are used to characterise the structure and distribution of acoustic energy in recordings of the natural environment. In particular it describes methods for noise reduction from recordings of the environment and a fast clustering algorithm used to estimate the spectral richness of long recordings.

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.

Characterisation of multi wall carbon nanotube–polymer composites for strain sensing applications

Carbon nanotubes (CNTs) have excellent electrical, mechanical and electromechanical properties. When CNTs are incorporated into polymers, electrically conductive composites with high electrical conductivity at very low CNT content (often below 1% wt CNT) result. Due to the change in electrical properties under mechanical load, carbon nanotube/polymer composites have attracted significant research interest especially due to their potential for application in in-situ monitoring of stress distribution and active control of strain sensing in composite structures or as strain sensors. To sucessfully develop novel devices for such applications, some of the major challenges that need to be overcome include; in-depth understanding of structure-electrical conductivity relationships, response of the composites under changing environmental conditions and piezoresistivity of different types of carbon nanotube/polymer sensing devices. In this thesis, direct current (DC) and alternating current (AC) conductivity of CNT-epoxy composites was investigated. Details of microstructure obtained by scanning electron microscopy were used to link observed electrical properties with structure using equivalent circuit modeling. The role of polymer coatings on macro and micro level electrical conductivity was investigated using atomic force microscopy. Thermal analysis and Raman spectroscopy were used to evaluate the heat flow and deformation of carbon nanotubes embedded in the epoxy, respectively, and related to temperature induced resistivity changes. A comparative assessment of piezoresistivity was conducted using randomly mixed carbon nanotube/epoxy composites, and new concept epoxy- and polyurethane-coated carbon nanotube films. The results indicate that equivalent circuit modelling is a reliable technique for estimating values of the resistance and capacitive components in linear, low aspect ratio-epoxy composites. Using this approach, the dominant role of tunneling resistance in determining the electrical conductivity was confirmed, a result further verified using conductive-atomic force microscopy analysis. Randomly mixed CNT-epoxy composites were found to be highly sensitive to mechanical strain and temperature variation compared to polymer-coated CNT films. In the vicinity of the glass transition temperature, the CNT-epoxy composites exhibited pronounced resistivity peaks. Thermal and Raman spectroscopy analyses indicated that this phenomenon can be attributed to physical aging of the epoxy matrix phase and structural rearrangement of the conductive network induced by matrix expansion. The resistivity of polymercoated CNT composites was mainly dominated by the intrinsic resistivity of CNTs and the CNT junctions, and their linear, weakly temperature sensitive response can be described by a modified Luttinger liquid model. Piezoresistivity of the polymer coated sensors was dominated by break up of the conducting carbon nanotube network and the consequent degradation of nanotube-nanotube contacts while that of the randomly mixed CNT-epoxy composites was determined by tunnelling resistance between neighbouring CNTs. This thesis has demonstrated that it is possible to use microstructure information to develop equivalent circuit models that are capable of representing the electrical conductivity of CNT/epoxy composites accurately. New designs of carbon nanotube based sensing devices, utilising carbon nanotube films as the key functional element, can be used to overcome the high temperature sensitivity of randomly mixed CNT/polymer composites without compromising on desired high strain sensitivity. This concept can be extended to develop large area intelligent CNT based coatings and targeted weak-point specific strain sensors for use in structural health monitoring.

Spectrum sensing, detection and optimisation in cognitive radio for non-stationary primary user signals

Cognitive radio is an emerging technology proposing the concept of dynamic spec- trum access as a solution to the looming problem of spectrum scarcity caused by the growth in wireless communication systems. Under the proposed concept, non- licensed, secondary users (SU) can access spectrum owned by licensed, primary users (PU) so long as interference to PU are kept minimal. Spectrum sensing is a crucial task in cognitive radio whereby the SU senses the spectrum to detect the presence or absence of any PU signal. Conventional spectrum sensing assumes the PU signal as ‘stationary’ and remains in the same activity state during the sensing cycle, while an emerging trend models PU as ‘non-stationary’ and undergoes state changes. Existing studies have focused on non-stationary PU during the transmission period, however very little research considered the impact on spectrum sensing when the PU is non-stationary during the sensing period. The concept of PU duty cycle is developed as a tool to analyse the performance of spectrum sensing detectors when detecting non-stationary PU signals. New detectors are also proposed to optimise detection with respect to duty cycle ex- hibited by the PU. This research consists of two major investigations. The first stage investigates the impact of duty cycle on the performance of existing detec- tors and the extent of the problem in existing studies. The second stage develops new detection models and frameworks to ensure the integrity of spectrum sensing when detecting non-stationary PU signals. The first investigation demonstrates that conventional signal model formulated for stationary PU does not accurately reflect the behaviour of a non-stationary PU. Therefore the performance calculated and assumed to be achievable by the conventional detector does not reflect actual performance achieved. Through analysing the statistical properties of duty cycle, performance degradation is proved to be a problem that cannot be easily neglected in existing sensing studies when PU is modelled as non-stationary. The second investigation presents detectors that are aware of the duty cycle ex- hibited by a non-stationary PU. A two stage detection model is proposed to improve the detection performance and robustness to changes in duty cycle. This detector is most suitable for applications that require long sensing periods. A second detector, the duty cycle based energy detector is formulated by integrat- ing the distribution of duty cycle into the test statistic of the energy detector and suitable for short sensing periods. The decision threshold is optimised with respect to the traffic model of the PU, hence the proposed detector can calculate average detection performance that reflect realistic results. A detection framework for the application of spectrum sensing optimisation is proposed to provide clear guidance on the constraints on sensing and detection model. Following this framework will ensure the signal model accurately reflects practical behaviour while the detection model implemented is also suitable for the desired detection assumption. Based on this framework, a spectrum sensing optimisation algorithm is further developed to maximise the sensing efficiency for non-stationary PU. New optimisation constraints are derived to account for any PU state changes within the sensing cycle while implementing the proposed duty cycle based detector.

Non-aqueous synthesis of hexagonal ZnO nanopyramids : gas sensing properties

Zinc oxide (ZnO) nanopyramids were synthesized by a one-pot route in a non-aqueous and surfactantfree environment. The synthesized metal oxide was characterized using SEM, XRD, and TEM to investigate the surface morphology and crystallographic phase of the nanostructures. It was observed that the ZnO nanopyramids were of uniform size and symmetrical, with a hexagonal base and height of ∼100 nm. Gas sensing characterization of the ZnO nanopyramids when deposited as thin-film onto conductometric transducers were performed towards NOx and C2H5OH vapor of different concentrations over a temperature range of 22–350 ◦C. It was observed that the sensors responded towards NO2 (10 ppm) and C2H5OH(250 ppm) analytes best at temperatures of 200 and 260 ◦C with a sensor response of 14.5 and 5.72, respectively. The sensors showed satisfactory sensitivity, repeatability as well as fast response and recovery towards both the oxidizing and the reducing analyte. The good performance was attributed to the low amount of organic impurities, large surface-to-volume ratio and high crystallinity of the solvothermally synthesized ZnO nanopyramids.

Vestibular rehabilitation following the removal of an acoustic neuroma : a systematic review of randomized trials

An acoustic neuroma (also known as a vestibular schwannoma) is an intracranial tumour of the vestibular nerve that is commonly treated by surgical resection. Following resection of an acoustic neuroma, patients may experience a range of symptoms that include deficits in gaze stability, mobility and balance. Vestibular rehabilitation may be useful in reducing the severity and minimizing the impact of these symptoms.

Social cars : sensing, gathering, sharing and conveying social cues to road users

Intelligent Transport Systems (ITS) resembles the infrastructure for ubiquitous computing in the car. It encompasses a) all kinds of sensing technologies within vehicles as well as road infrastructure, b) wireless communication protocols for the sensed information to be exchanged between vehicles (V2V) and between vehicles and infrastructure (V2I), and c) appropriate intelligent algorithms and computational technologies that process these real-time streams of information. As such, ITS can be considered a game changer. It provides the fundamental basis of new, innovative concepts and applications, similar to the Internet itself. The information sensed or gathered within or around the vehicle has led to a variety of context-aware in-vehicular technologies within the car. A simple example is the Anti-lock Breaking System (ABS), which releases the breaks when sensors detect that the wheels are locked. We refer to this type of context awareness as vehicle/technology awareness. V2V and V2I communication, often summarized as V2X, enables the exchange and sharing of sensed information amongst cars. As a result, the vehicle/technology awareness horizon of each individual car is expanded beyond its observable surrounding, paving the way to technologically enhance such already advanced systems. In this chapter, we draw attention to those application areas of sensing and V2X technologies, where the human (driver), the human’s behavior and hence the psychological perspective plays a more pivotal role. The focal points of our project are illustrated in Figure 1: In all areas, the vehicle first (1) gathers or senses information about the driver. Rather than to limit the use of such information towards vehicle/technology awareness, we see great potential for applications in which this sensed information is then (2) fed back to the driver for an increased self-awareness. In addition, by using V2V technologies, it can also be (3) passed to surrounding drivers for an increased social awareness, or (4), pushed even further, into the cloud, where it is collected and visualized for an increased, collective urban awareness within the urban community at large, which includes all city dwellers.

Sensing and control for a small-size helicopter

This paper details the progress to date, toward developing a small autonomous helicopter. We describe system architecture, avionics, visual state estimation, custom IMU design, aircraft modelling, as well as various linear and neuro/fuzzy control algorithms. Experimental results are presented for state estimation using fused stereo vision and IMU data, heading control, and attitude control. FAM attitude and velocity controllers have been shown to be effective in simulation.

Improving the hydrogen gas sensing performance of Pt/MoO3 nanoplatelets using a nano thick layer of La2O3

In this paper, we present how a thin RF sputtered layer of lanthanum oxide (La2O3) can alter electrical and improve hydrogen gas sensing characteristics of Pt/molybdenum oxide (MoO3) nanostructures Schottky diodes. We derived the barrier height, ideality factor and dielectric constant from the measured I–V characteristics at operating temperatures in the range of 25–300 ◦C. The dynamic response, response and recovery times were obtained upon exposure to hydrogen gas at different concentrations. Analysis of the results indicated a substantial improvement to the voltage shift sensitivity of the sensors incorporating the La2O3 layer. We associate this enhancement to the formation of numerous trap states due to the presence of the La2O3 thin film on the MoO3 nanoplatelets. These trap states increase the intensity of the dipolar charges at the metal–semiconductor interface, which induce greater bending of the energy bands. However, results also indicate that the presence of La2O3 trap states also increases response and recover times as electrons trapping and de-trapping processes occur before they can pass through this thin dielectric layer.

Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Hydrogen Gas Sensor Based on Highly Ordered Polyaniline/Multiwall Carbon Nanotubes Composite

In this work, the structural and gas sensing properties of an electropolymerized, polyaniline (PANI)/multiwall carbon nanotube (MWNT) composite based surface acoustic wave (SAW) sensor are reported. Thin films made of PANI nanofibers were deposited onto 36 lithium tantalate (LiTaO3) SAW transducers using electropolymerization and were subsequently dedoped. Scanning electron microscopy (SEM) revealed the compact growth of the composites which is much denser than that of PANI nanofibers. The PANI/MWNT composite based SAW sensor was then exposed to different concentrations of hydrogen (H2) gas at room temperature with a demonstrated electrical response.