Structural health monitoring of wind turbine blades: acoustic source localization using wireless sensor networks

Structural health monitoring (SHM) is important for reducing the maintenance and operation cost of safety-critical components and systems in offshore wind turbines. This paper proposes an in situ wireless SHM system based on an acoustic emission (AE) technique. By using this technique a number of challenges are introduced due to high sampling rate requirements, limitations in the communication bandwidth, memory space, and power resources. To overcome these challenges, this paper focused on two elements: (1) the use of an in situ wireless SHM technique in conjunction with the utilization of low sampling rates; (2) localization of acoustic sources which could emulate impact damage or audible cracks caused by different objects, such as tools, bird strikes, or strong hail, all of which represent abrupt AE events and could affect the structural health of a monitored wind turbine blade. The localization process is performed using features extracted from aliased AE signals based on a developed constraint localization model. To validate the performance of these elements, the proposed system was tested by testing the localization of the emulated AE sources acquired in the field

Design of an instrumented smart cutting tool and its implementation and application perspectives

This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Graphene-Based Acousto-Optic Sensors with Vibrating Resonance Energy Transfer and Applications

Graphene as a two-dimensional planar material has numerous advantages for realizing high-performance nano-electromechanical systems (NEMS) such as nanoscale sensors including strain sensors, optical modulators or energy harvesters. Large Young’s modulus (1 TPa for single layer graphene), ultra-low weight, low residual stress and large breaking strength properties are important properties as two-dimensional (2D) ultrathin resonators. Graphene resonators are recently utilized for low complexity design of nanoscale acousto-optic sensors based on a novel theoretical model describing vibrating Förster resonance energy transfer (VFRET) mechanism. Proposed system combines the advantages of graphene with quantum dots (QDs) as donor and acceptor pairs with broad absorption spectrum, large cross-sections, tunable emission spectra, size-dependent emission wavelength, high photochemical stability and improved quantum yield. Device structure supporting wide-band resonance frequencies including acoustic and ultrasound ranges promises high-performance applications for challenging environments. Remote sensors and acousto-optic communication channels are formed for in-body applications, wireless body area sensor networks (WBASNs), space and interplanetary systems, microfluidics and visible light communication (VLC)-based architectures

A multisensing setup for the intelligent tire monitoring

The present paper offers the chance to experimentally measure, for the first time, the internal tire strain by optical fiber sensors during the tire rolling in real operating conditions. The phenomena that take place during the tire rolling are in fact far from being completely understood. Despite several models available in the technical literature, there is not a correspondently large set of experimental observations. The paper includes the detailed description of the new multi-sensing technology for an ongoing vehicle measurement, which the research group has developed in the context of the project OPTYRE. The experimental apparatus is mainly based on the use of optical fibers with embedded Fiber Bragg Gratings sensors for the acquisition of the circumferential tire strain. Other sensors are also installed on the tire, such as a phonic wheel, a uniaxial accelerometer, and a dynamic temperature sensor. The acquired information is used as input variables in dedicated algorithms that allow the identification of key parameters, such as the dynamic contact patch, instantaneous dissipation and instantaneous grip. The OPTYRE project brings a contribution into the field of experimental grip monitoring of wheeled vehicles, with implications both on passive and active safety characteristics of cars and motorbikes