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

INE/AUTC 10.0

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

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)

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Towards Intelligent Tire and Self-Powered Sensing Systems

Tires are the interface between a vehicle and the ground providing forces and isolation to the vehicle. For vehicle safety, stability, maintenance, and performance, it is vital to estimate or measure tire forces, inflation pressure, and contact friction coefficient. Estimation methods can predict tire forces to some extent however; they fail in harsh maneuvers and are dependent on road surface conditions for which there is no robust estimation method. Measurement devices for tire forces exist for vehicle testing but at the cost of tens of thousands of dollars. Tire pressure-monitoring sensors (TPMS) are the only sensors available in newer and higher end vehicles to provide tire pressure, but there are no sensors to measure road surface condition or tire forces for production vehicles. With the prospect of autonomous driving on roads in near future, it is paramount to make the vehicles safe on any driving and road condition. This is only possible by additional sensors to make up for the driver’s cognitive and sensory system. Measuring road condition and tire forces especially in autonomous vehicles are vital in their safety, reliability, and public confidence in automated driving. Real time measurement of road condition and tire forces in buses and trucks can significantly improve the safety of road transportation system, and in miming/construction and off-road vehicles can improve performance, tire life and reduce operational costs. In this thesis, five different types of sensors are designed, modelled, optimized and fabricated with the objective of developing an intelligent tire. In order to design these sensors,~both electromagnetic generator (EMG) and triboelectric nanogenerators (TENG) are used. In the first two initial designed sensors, with the combination of EMG and TENG into a single package, two hybridized sensors are fabricated with promising potential for self-powered sensing. The potential of developed sensors are investigated for tire-condition monitoring system (TCMS). Considering the impressive properties of TENG units of the developed hybridized devices, three different flexible nanogenerators, only based on this newly developed technology, are developed for TCMS. The design, modelling, working mechanism, fabrication procedure, and experimental results of these TENG sensors are fully presented for applications in TCMS. Among these three fabricated sensors, one of them shows an excellent capability for TCMS because of its high flexibility, stable and high electrical output,and an encapsulated structure. The high flexibility of developed TENG sensor is a very appealing feature for TCMS, which cannot be found in any available commercial sensor. The fabricated TENG sensors are used for developing an intelligent tire module to be eventually used for road testing. Several laboratory and road tests are performed to study the capability of this newly developed TENG-based sensor for tire-condition monitoring system. However the development of this sensor is in its early stage, it shows a promising potential for installation into the hostile environment of tires and measuring tire-road interacting forces. A comparative studies are provided with respect to Michigan Scientific transducer to investigate the potential of this flexible nanogenerator for TCMS. It is worth mentioning that this PhD thesis presents one of the earliest works on the application of TENG-based sensor for a real-life system. Also, the potential of commercially available thermally and mechanically durable Micro Fiber Composite (MFC) sensor is experimentally investigated for TCMS with fabricating another set of intelligent tire. Several testing scenarios are performed to examine the potential of these sensors for TCMS taking into account a simultaneous measurement from Michigan Scientific transducer. Although both flexibility and the cost of this sensor is not comparable with the fabricated TENG device, they have shown a considerable and reliable performance for online measuring of tire dynamical parameters in different testing scenarios, as they can be used for both energy harvesting and sensing application in TCMS. The extensive road testing results based on the MFC sensors provide a valuable set of data for future research in TCMS. It is experimentally shown that MFC sensor can generate up to 1.4 $\mu W$ electrical power at the speed of 28 $[kph]$. This electrical output shows the high capability of this sensor for self-powered sensing application in TCMS. Results of this thesis can be used as a framework by researchers towards self-powered sensing system for real-world applications such as intelligent tires