Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Advanced Energy Harvesting Technologies

Energy harvesting is the conversion of unused or wasted energy in the ambient environment into useful electrical energy. It can be used to power small electronic systems such as wireless sensors and is beginning to enable the widespread and maintenance-free deployment of Internet of Things (IoT) technology. This Special Issue is a collection of the latest developments in both fundamental research and system-level integration. This Special Issue features two review papers, covering two of the hottest research topics in the area of energy harvesting: 3D-printed energy harvesting and triboelectric nanogenerators (TENGs). These papers provide a comprehensive survey of their respective research area, highlight the advantages of the technologies and point out challenges in future development. They are must-read papers for those who are active in these areas. This Special Issue also includes ten research papers covering a wide range of energy-harvesting techniques, including electromagnetic and piezoelectric wideband vibration, wind, current-carrying conductors, thermoelectric and solar energy harvesting, etc. Not only are the foundations of these novel energy-harvesting techniques investigated, but the numerical models, power-conditioning circuitry and real-world applications of these novel energy harvesting techniques are also presented

Energy Harvesters and Self-powered Sensors for Smart Electronics

This book is a printed edition of the Special Issue “Energy Harvesters and Self-Powered Sensors for Smart Electronics” that was published in Micromachines, which showcases the rapid development of various energy harvesting technologies and novel devices. In the current 5G and Internet of Things (IoT) era, energy demand for numerous and widely distributed IoT nodes has greatly driven the innovation of various energy harvesting technologies, providing key functionalities as energy harvesters (i.e., sustainable power supplies) and/or self-powered sensors for diverse IoT systems. Accordingly, this book includes one editorial and nine research articles to explore different aspects of energy harvesting technologies such as electromagnetic energy harvesters, piezoelectric energy harvesters, and hybrid energy harvesters. The mechanism design, structural optimization, performance improvement, and a wide range of energy harvesting and self-powered monitoring applications have been involved. This book can serve as a guidance for researchers and students who would like to know more about the device design, optimization, and applications of different energy harvesting technologies

Wireless and Electromechanical Approaches for Strain Sensing and Crack Detection in Fiber Reinforced Cementitious Materials.

High performance fiber reinforced cementitious composites (HPFRCC) are novel cement-based construction materials with excellent mechanical behaviors. As these new materials begin to be introduced into practice, there is a need to monitor the health and performance of structures and structural elements made of them. This thesis explores two novel approaches to sensing strain and cracking in HPFRCC structural elements: wireless sensors and the use of HPFRCC materials as their own sensor platform. First, wireless monitoring systems are explored because they are relatively low-cost and easy to install. Illustration of a wireless monitoring system using a large number of wireless sensor nodes is provided using the Grove Street Bridge located in Ypsilanti, Michigan. The computational resources of the wireless sensor are leveraged to locally process response data recorded from an HPFRCC element. Damage index methods previously tailored for HPFRCC structural components are embedded into the wireless sensors for automated damage detection. The utility of locally processing response data at the sensor is validated using a cyclically loaded HPFRCC bridge pier. While wireless sensors are capable of automated data interrogation, they do not fully quantify cracking in HPFRCC elements. To address this limitation, the inherent electromechanical properties of HPFRCC materials are harnessed. This work undertakes detail experimental evaluation of the electromechanical properties of one class of strain-hardening HPFRCC: engineered cementitious composites (ECC). First, the piezoresistive properties of ECC are quantified through two- and four-point probe methods. While strain can be accurately measured in the material’s elastic regime, microcracking during strain hardening prevents correlations between resistivity and strain to be accurately made. Electrical impedance tomography (EIT) is proposed to map the spatial distribution of ECC bulk conductivity in two-dimensions using repeated electrical measurements taken at the specimen boundary. Hence, EIT conductivity maps can serve as a tool for measuring strain fields in ECC plate elements as well as for imaging cracking in fine detail. The EIT sensing approach can also be applied to any semi-conductive material to map conductivity. The universality of the approach is illustrated using a carbon nanotube composite material as a sensing skin, or appliqué, for structural health monitoring.Ph.D.Civil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61606/1/tschou_1.pd

Graphene/P(VDF-TrFE) Heterojunction Based Wearable Sensors with Integrated Piezoelectric Energy Harvester

Graphene, with its outstanding material properties, including high carrier mobility, atomically thin nature, and ability to tolerate mechanical deformation related strain up to 20% before breaking, make it very attractive for developing highly sensitive and conformable strain/pressure sensor for wearable electronics. Unfortunately, graphene by itself is not piezoresistive, so developing a strain sensor utilizing just graphene is challenging. Fortunately, graphene synthesized on Cu foil can be transferred to arbitrary substrates (preserving its high quality), including flexible polymer substrates, which will allow the overall flexibility and conformability of the sensing element, to be maintained. Furthermore, a graphene/polymer based sensor devices can be easily patterned into an array over dimensions reaching several feet, taking advantage of large area synthesis of graphene, which will make the ultimate sensor very inexpensive. If a piezo-electric polymer, such as P(VDF-TrFE), is chosen to form a heterojunction with graphene, it will strongly affect the carrier density in graphene, due to the fixed charge developing on its surface under strain or pressure. Taking advantage of the high carrier mobility in graphene, such a charge change can result in very high sensitivity to pressure and strain. Hence, these features, coupled with the flexible nature of the device and ease of fabrication, make it a very attractive candidate for use in the growing wearable technology market, especially biomedical applications and smart health monitoring system as well as virtual reality sensors. In this dissertation, various unique properties of graphene and P(VDF-TrFE), and their current applications and trends are discussed in chapter 1. Additionally, synthesis of graphene and P(VDF-TrFE) and their characterizations by various techniques are investigated in chapter 2. Based on piezoelectric property of P(VDF-TrFE), a highly flexible energy harvesters on PDMS as well as PET substrates have been developed and demonstrated their performances in chapter 3. As follow-up research, graphene/P(VDF-TrFE) heterojunction based wearable sensors with integrated piezoelectric energy harvester on flexible substrates have also been fabricated and demonstrated for practical wearable application in chapter 4. Finally, major findings and future directions of the project are discussed in chapter 5

Vibration Energy Harvesting for Wireless Sensors

Kinetic energy harvesters are a viable means of supplying low-power autonomous electronic systems for the remote sensing of operations. In this Special Issue, through twelve diverse contributions, some of the contemporary challenges, solutions and insights around the outlined issues are captured describing a variety of energy harvesting sources, as well as the need to create numerical and experimental evidence based around them. The breadth and interdisciplinarity of the sector are clearly observed, providing the basis for the development of new sensors, methods of measurement, and importantly, for their potential applications in a wide range of technical sectors

Advanced structural health monitoring strategies for condition-based maintenance planning of offshore wind turbine support structures

Condition-based maintenance strategies need to be adopted as distance-to-shore and water depth increase in the offshore wind industry. The aim of the research presented herein is to develop advance structural health monitoring strategies that enhance the condition-based maintenance of offshore wind turbine support structures. The focus is on the selection of technologies, the implementation process, the analysis of the asset’s structural response under complex loading, the economic justification for structural health monitoring implementation and the effective structural health monitoring data analysis. Research activities consist of the provision of a comprehensive study for structural health monitoring technologies’ utilisation in the offshore wind industry. This is followed by parametric structural modelling, simulation and validation of an operational offshore wind turbine tower, support structure and soil-structure interaction, using commercial software. The evaluation of the asset’s response under complex loading subject to design changes and failure mechanisms is also undertaken. A combination of existing and newly developed methodologies is deployed for the effective data management of structural health monitoring systems and validated with industrial data for the case of strain monitoring. These include unsupervised learning algorithms (neural networks), deterministic and probabilistic methods for noise cleansing and missing data imputation. Guidelines for the structural health monitoring implementation from design stage of a wind farm are proposed and applied to a baseline scenario. This is utilised to assess the economic impact that structural health monitoring has in the lifecycle of the assets. The achieved results show that the implementation of structural health monitoring in offshore wind turbine following the Statistical Pattern Recognition paradigm and the proposed guidelines has the potential to reduce the Operational Expenditure. This reduction is much greater than the cost associated with the implementation of these systems. Monitoring from the commissioning of the assets is crucial for the system’s calibration and establishing thresholds. The developed noise cleansing and missing data imputation methodologies can successfully be employed together to produce more complete low-disturbed datasets

Modern Telemetry

Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

Engineering for a Changing World: 59th IWK, Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 11-15, 2017 : programme

In 2017, the Ilmenau Scientific Colloquium is again organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, complemented by workshops, is characterised by the following topics, but not limited to them: – Precision Engineering and Metrology – Industry 4.0 and Digitalisation in Mechanical Engineering – Mechatronics, Biomechatronics and Mechanism Technology – Systems Technology – Innovative Metallic Materials The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university