Informative bayesian tools for damage localisation by decomposition of Lamb wave signals

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation, thanks to some distinct advantages. Guided waves, in particular Lamb waves, can be used to localise damage by utilising prior knowledge of propagation and reflection characteristics. Typical localisation methods make use of the time of arrival of waves emitted or reflected from the damage, the simplest of which involves triangulation (with a known wave speed). In order to obtain reflection information, it is useful to decompose the measured signal into the expected waves propagating directly from the actuation source in the absence of damage, called a baseline, and for this paper referred to as nominal waves. This decomposition allows for determination of the residual signal which contains only waves from reflection sources such as damage, boundaries or other local inhomogeneities. Previous decomposition methods make use of accurate analytical models, but there is a gap in methods of decomposition for complex materials and structures. A new method is shown here which uses a Bayesian approach to decompose single-source signals, requiring only prior information on surface displacement along the propagation path. This Bayesian decomposition has the advantage of generating a distribution of possible nominal signals and allows for quantification of the uncertainty of the expected signal. Furthermore, the approach produces inherent parametric features which correlate to known physics of guided waves, and likelihood estimates can be used to assess the quality of the decomposition. In this paper, the decomposition method is demonstrated on data from a simulation of guided wave propagation in a small aluminium plate, using the local interaction simulation approach, for a damaged and undamaged case. Analysis of the decomposition method is done in three ways; inspect individual decomposed signals, track the inherently produced parametric features along propagation distance, and use method in a localisation strategy. The localisation method is demonstrated using the decomposed signal at several sensor locations and triangulates for the source of reflected waves from damage. The Bayesian decomposition was found to work well in returning signals containing only reflected waves, as well as obtaining parametric features that can be used to assess damage and confidence in the decomposed wave. The use of these waves in the localisation method returned estimates accurate to within 1 mm in many sensor configurations. Leading on from the work shown here, the paper finishes with future work; the authors intend to extend this method to scenarios where less prior knowledge is available

Passive sensing of low velocity impact damage on composite panels in simulated environmental and operational conditions

During its lifetime, an aircraft structure is subjected to impacts from a wide range of sources (e.g. bird strike, tool drop, hail). For composite structures, low velocity impacts may generate Barely Visual Impact Damage (BVID) that is difficult to assess without complex inspection techniques and may significantly reduce the strength of the structure. To reduce the need for costly inspection techniques, there is interest in developing integrated Structural Health Monitoring (SHM) systems to monitor the occurrence of impacts and provide location and severity estimations to allow the operators to make maintenance decisions (e.g. repairs). Many methods have been developed to achieve this using information encoded in impact induced strain waves recorded by sensors attached to the structure. However, these so-called passive methods have mostly been developed in laboratory conditions and do not consider operational and environmental in-service conditions which may significantly influence the accuracy and feasibility of these estimates. The research reported in this thesis was conducted to address some of the main challenges associated of with passive SHM application for in-service conditions, specifically accuracy, robustness, reliability and feasibility. A data driven approach was taken as it allows flexibility in scaling up from simple coupons to complex structures. Novel signal processing and reference database methods were proposed for accurate and robust impact location and maximum impact force estimation which only requires data from a single reference impact case, making it feasible for the variable conditions in service. A novel kriging based extension to these methods was developed to allow uncertainty quantification that provides more reliable information to the operator. A multifidelity approach to constructing the reference database for the data driven approach was also developed to significantly reduce the requirement of experimental sampling with alternative data sources and increase feasibility. Lastly, the developed methods were tested on prototype data acquisition units to demonstrate applicability on in-service hardware. The results of this thesis have provided novel improvements to the data driven passive SHM approach which significantly increases its applicability for aircraft structures.Open Acces

MIMO OFDM DOA Estimation Algorithm Implementation and Validation Using SDR Platform

The work is devoted to research in Mobile Station (MS) positioning techniques having opportunity and perspective of using in the next generation communication networks, particularly in cellular networks. Direction of Arrival (DOA) estimation is necessary in many positioning applications and has been well studied by academy and by industry. The main contribution of this work is to design and implement a computationally light direction of arrival estimator on a Multiple Input Multiple Output (MIMO) Software Defined Radio (SDR) platform. Implemented direction of arrival estimator is tested and validated in real conditions and experimental measurements show, that the implemented algorithm can accurately estimate the directions of arrived signals. Used algorithm, estimates direction of arrivals by processing received data of 16-element two dimensional planar antenna array. The algorithm uses initial data received from channel estimators and further process it to obtain direction information. The hardware implementation has been thoroughly analyzed and experimentally validated and open source host code is available on GitHub

MIMO OFDM DOA Estimation Algorithm Implementation and Validation Using SDR Platform

The work is devoted to research in Mobile Station (MS) positioning techniques having opportunity and perspective of using in the next generation communication networks, particularly in cellular networks. Direction of Arrival (DOA) estimation is necessary in many positioning applications and has been well studied by academy and by industry. The main contribution of this work is to design and implement a computationally light direction of arrival estimator on a Multiple Input Multiple Output (MIMO) Software Defined Radio (SDR) platform. Implemented direction of arrival estimator is tested and validated in real conditions and experimental measurements show, that the implemented algorithm can accurately estimate the directions of arrived signals. Used algorithm, estimates direction of arrivals by processing received data of 16-element two dimensional planar antenna array. The algorithm uses initial data received from channel estimators and further process it to obtain direction information. The hardware implementation has been thoroughly analyzed and experimentally validated and open source host code is available on Github

A Combined Batteryless Radio and WiFi Indoor Positioning for Hospital Nursing

This paper proposes a design of an efficient hospital nurse calling system which combines two types of indoor localization systems. The purpose of the first system is to locate patients while the second is to locate nurses equipped with their smart phones. The main goal of developing such system is to decrease the time taking for nurses to provide healthcare for patients. Patients' positioning system is RF based. Indeed, each patient is equipped with a wireless and battery-free call button. When the switch is pressed, a wireless telegram is sent to reference nodes that act like Wireless Sensor Networks (WSN). The positioning of patient is performed using trilateration method with the help of Received Signal Strength Indicator (RSSI) values. Hence, beacons will forward the received signal from patient’s call button to a central receiver module connected to a computer. A dedicated program has been developed to calculate the position of the call button and post it on an online database. On the other hand, the nurses’ localization system is WiFi-based. Nurses' positioning is done by determining the Time of Arrival (ToA) and the Angle of Arrival (AoA) between the mobile phone and the WiFi router. The mobile phone locations are posted to the online database as well. Our program performs a comparison between the nurses' and the patient's coordinates. The nearest nurse gets an alarm. As consequence, a patient gets care from the nearest available nurse in an efficient way and with less time. The proposed system is user-friendly and Internet of Things (IoT) based architecture integrating two heterogeneous localization systems seamlessly