Aircraft structures exhibit localized vibrations over a wide range of frequencies. Such vibrations can be used to power sensors which then monitor the health of the structure. Conventional vibrational piezoelectric harvesting involves optimizing the harvester for one distinct frequency. The aim of this work is to design a wireless vibrational piezoelectric system capable of energy harvesting in the range of 100–500 Hz by tailoring the resonant behavior of cantilever structures. We herein employ a model capable of predicting the performance of a piezoelectric cantilever retrofit on a structural health monitoring sensor and then formulate a design optimization problem and solve with the level set topology optimization method. The designs are verified through fabrication of experimental prototypes.

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Summary Acoustic emission (AE) is the spontaneous release of energy caused by the growth of damage, the monitoring of which gives an indication of the presence of damage within a structure. The current standard for AE localisation is difficult to apply in a low‐power system as sensors must either be wired together or Node's time synchronised, which is power intensive. This paper proposes the use of a method of bonding three piezoelectric sensors in a small triangular array, which has previously been shown by Aljets et al. to be capable of locating sources in simple structures. In this prior work the wave's A0 mode was used to predict the angle of arrival and the distance the wave has travelled through single sensor modal analysis. This paper presents the development of hardware to apply this technique and testing that showed artificial sources could be located in simple plates to a good level of accuracy. The addition of complexity to structures significantly reduced accuracy. This prompted hardware modifications to use the S0 mode for angle prediction. Testing showed that this significantly improved performance in a complex composite structure. The power consumption of the device is very low, consuming 0.33 mW in sleep mode, 17.44 mW whilst waiting for an event and 38 mW to record, process and transmit an event. This level of consumption has the potential to be self‐powered via energy harvesting.

Anthropogenic noise is a pervasive global pollutant that has been detected in every major habitat on the planet. Detrimental impacts of noise pollution on physiology, immunology and behaviour have been shown in terrestrial vertebrates and invertebrates. Equivalent research on aquatic organisms has until recently been stunted by the misnomer of a silent underwater world. In fish, however, noise pollution can lead to stress, hearing loss, behavioural changes and impacted immunity. But, the functional effects of this impacted immunity on disease resistance due to noise exposure have remained neglected. Parasites that cause transmissible disease are key drivers of ecosystem biodiversity and a significant factor limiting the sustainable expansion of the animal trade. Therefore, understanding how a pervasive stressor is impacting host–parasite interactions will have far-reaching implications for global animal health. Here, we investigated the impact of acute and chronic noise on vertebrate susceptibility to parasitic infections, using a model host–parasite system (guppy– Gyrodactylus turnbulli ). Hosts experiencing acute noise suffered significantly increased parasite burden compared with those in no noise treatments. By contrast, fish experiencing chronic noise had the lowest parasite burden. However, these hosts died significantly earlier compared with those exposed to acute and no noise treatments. By revealing the detrimental impacts of acute and chronic noise on host–parasite interactions, we add to the growing body of evidence demonstrating a link between noise pollution and reduced animal health.

This chapter covers the overview of requirements arising in the aerospace industry for operating a structural health monitoring (SHM) system. The requirements are based on existing standards and guidelines and include both requirements on the physical components of the system (such as sensors, data acquisition systems and connectors) and their functional requirements (such as reliability, confidence measure and probability of detection). Emphasis has been given to on-board and ground-based components because they have different functionality requirements. An important factor in the reliability of the system is the effect of the environment and operational loads on the reliability of the diagnosis and, consequently, prognosis. The recommended guidelines for testing the reliability of the system under varying operational conditions are presented. This chapter is then finalized by reporting on methodologies for optimal sensor number and placement, based on different sensor technologies and different optimization algorithms.

Present-day technologies used in SHM (Structural Health Monitoring) systems in many implementations are based on wireless sensor networks (WSN). In the context of the continuous development of these systems, the costs of the elements that form the monitoring system are decreasing. In this situation, the challenge is to select the optimal number of sensors and the network architecture, depending on the wireless system’s other parameters and requirements. It is a challenging task for WSN to provide scalability to cover a large area, fault tolerance, transmission reliability, and energy efficiency when no events are detected. In this article, fundamental issues concerning wireless communication in structural health monitoring systems (SHM) in the context of non-destructive testing sensors (NDT) were presented. Wireless technology developments in several crucial areas were also presented, and these include engineering facilities such as aviation and wind turbine systems as well as bridges and associated engineering facilities.

Acoustic emission (AE) technology is a non-destructive testing (NDT) technique that is able to monitor the process of hydrogen-induced cracking (HIC). AE uses piezoelectric sensors to convert the elastic waves generated from the growth of HIC into electric signals. Most piezoelectric sensors have resonance and thus are effective for a certain frequency range, and they will fundamentally affect the monitoring results. In this study, two commonly used AE sensors (Nano30 and VS150-RIC) were used for monitoring HIC processes using the electrochemical hydrogen-charging method under laboratory conditions. Obtained signals were analyzed and compared on three aspects, i.e., in signal acquisition, signal discrimination, and source location to demonstrate the influences of the two types of AE sensors. A basic reference for the selection of sensors for HIC monitoring is provided according to different test purposes and monitoring environments. Results show that signal characteristics from different mechanisms can be identified more clearly by Nano30, which is conducive to signal classification. VS150-RIC can identify HIC signals better and provide source locations more accurately. It can also acquire low-energy signals better, which is more suitable for monitoring over a long distance.

Background: To develop experience, orthopedic surgeons train their own proprioception to detect torque during screw insertion. This experience is acquired over time and when implanting conventional/non-locked screws in osteopenic cancellous bone the experienced surgeon still strips between 38-45%. Technology needs to be investigated to reduce stripping rates. Acoustic-Emission technology has the ability to detect stress wave energy transmitted through a screw during insertion into synthetic bone. Our hypothesis is Acoustic-Emission waves can be detected through standard orthopedic screwdrivers while advancing screws through purchase and overtightening in cancellous human bone with different bone mineral densities replicating the clinical state.Methods: 77 non-locking 4mm and 6.5mm diameter cancellous bone screws were inserted through to stripping into the lateral condylar area of 6 pairs of embalmed distal femurs. Specimens had varying degrees of bone mineral density determined by quantitative CT. Acoustic-Emission energy and axial force were detected for each test. Results:The tests showed a significant high correlation between bone mineral density and Acoustic-Emission energy with R=0.74. A linear regression model with the mean stripping load as the dependent variable and mean Acoustic-Emission energy, bone mineral densities and screw size as the independent variables resulted in r 2 =0.94.Interpretation: This experiment succeeded in testing real time Acoustic-Emission monitoring of screw purchase and overtightening in human bone. Acoustic-Emission energy and axial compressive force have positive high correlation to bone mineral density. The purpose is to develop a known technology and apply it to improve the bone-metal construct strength by reducing human error of screw overtightening.