Bio: Michael Reyer is an academic researcher. The author has contributed to research in topics: Wireless sensor network & Condition monitoring. The author has an hindex of 2, co-authored 3 publications receiving 46 citations.
••01 Nov 2011
TL;DR: In this article, a wireless sensor network for Structural Health Monitoring using commercially available wireless sensors to measure and extract vibration characteristics of bridges is proposed, and the functionality of the network is verified in a laboratory experiment.
Abstract: Bridges are bottlenecks in the railroad net, because of their limiting characteristics. To achieve a high load of the railroads old bridges especially are being pushed to their physical limit, regarding transfer speed, schedule, axle load and train length. Therefore monitoring of these strategic structures is getting more and more important. Installation costs of conventional sensors are expensive and time intensive. New wireless sensor platforms and distributed processing algorithms, going hand in hand with new or enhanced monitoring methods, promise an early damage detection and damage estimation. This paper designs a wireless sensor network for Structural Health Monitoring using commercially available wireless sensors to measure and extract vibration characteristics of bridges. The functionality of the network is verified in a laboratory experiment.
TL;DR: Through this research, it can be seen that the WSN is an effective tool for structural monitoring in historic preservation.
Abstract: The preservation of the history of the United States through its significant buildings is critical; however, this initiative is currently threatened due to the modernization of the nation’s infrastructure. If a fast and cost-effective way to monitor the condition of a historic structure existed, many more structures could be rehabilitated for modern uses while preserving the important historic content. Widely accessible wireless sensor network (WSN) technology could be a great asset to the preservation of historic structures in the future. The main objectives of this work are to develop a reliable WSN that is tailored for use in historic structures, and to implement the system in a structure undergoing rehabilitation. The structure considered is an historic wooden church in which the foundation requires replacement. Sensors will monitor tilt of the church’s walls throughout construction. During the construction process, the entire floor of the church is removed and the tree stump foundations are replaced by concrete masonry unit (CMU) blocks and steel pedestals. The tilt in the walls is correlated to the construction process. Through this research, it can be seen that the WSN is an effective tool for structural monitoring in historic preservation.
TL;DR: The main goals of this study are to develop a wireless sensor network (WSN) for the specific application of historic structures and to conduct a feasibility test in the field.
Abstract: Preserving significant buildings not only saves the structure, but also the history of the United States for future generations. The availability of a fast and cost-effective monitoring system could help persuade more people to rehabilitate historic structures rather than building a new structure in its place. The main goals of this study are to develop a wireless sensor network (WSN) for the specific application of historic structures and to conduct a feasibility test in the field. The structure considered is a historic masonry church with a timber-framed roof. During the construction process, the foundations along the exterior walls are underpinned and the floors are removed and replaced with a floating concrete slab, topped with stone. On completion of the structural foundation work, the WSN is evident as an effective instrument for monitoring of historic structures.
TL;DR: Practical engineering solutions are focused on which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies, which identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.
Abstract: In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.
TL;DR: It is shown that, with appropriate integration, the device will have sufficient energy to operate perpetually in a distributed WSHM application and that energy autonomy is comfortably achievable for duty cycles up to 0.75%, meeting the demands of the application, and up to 1.5%, invoking the FC.
Abstract: This paper presents the design, implementation, and characterization of a hardware platform applicable to wireless structural health monitoring (WSHM). The primary design goal is to devise a system capable of persistent operation for the duration of the life cycle of a target structure. It should be deployable during the construction phase and reconfigurable thereafter, suitable for continuous long-term monitoring. In addition to selecting the most energy efficient useful components to ensure the lowest possible power consumption, it is necessary to consider sources of energy other than, or complementary to, batteries. Thus, the platform incorporates multisource energy harvesting, electrochemical fuel cell (FC), energy storage, recharging capability, and intelligent operation through real-time energy information exchange with the primary controller. It is shown that, with appropriate integration, the device will have sufficient energy to operate perpetually in a distributed WSHM application. This conclusion is demonstrated through experimental results, simulations, and empirical measurements that demonstrate the high-efficiency energy conversion of the harvesters (up to 86%) and low-power characteristics of the platform (less than 1 mW in sleep mode). It is shown that energy autonomy is comfortably achievable for duty cycles up to 0.75%, meeting the demands of the application, and up to 1.5%, invoking the FC.
TL;DR: In this article, a review of the most commonly adopted bridge fault detection methods is presented, focusing on model-based finite element updating strategies, non-model-based (data-driven) fault detection method, such as artificial neural network, and Bayesian belief network-based structural health monitoring methods.
Abstract: Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted.
••27 Mar 2014
TL;DR: A novel set of value added services, aimed at developing a set of modules which can facilitate the diagnosis for the doctors through tele-monitoring of patients, are introduced through this paper.
Abstract: This paper illustrates the design and implementation of an e-health monitoring networked system. The architecture for this system is based on smart devices and wireless sensor networks for real time analysis of various parameters of patients. This system is aimed at developing a set of modules which can facilitate the diagnosis for the doctors through tele-monitoring of patients. It also facilitates continuous investigation of the patient for emergencies looked over by attendees and caregivers. A set of medical and environmental sensors are used to monitor the health as well as the surrounding of the patient. This sensor data is then relayed to the server using a smart device or a base station in close proximity. The doctors and caregivers monitor the patient in real time through the data received through the server. The medical history of each patient including medications and medical reports are stored on cloud for easy access and processing for logistics and prognosis of future complications. The architecture is so designed for monitoring a unitary patient privately at home as well as multiple patients in hospitals and public health care units. Use of smartphones to relay data over internet reduces the total cost of the system. We have also considered the privacy and security aspects of the system keeping the provision for selective authority for patients and their relatives to access the cloud storage as well as the possible threats to the system. We have also introduced a novel set of value added services through this paper which include Real Time Health Advice and Action (ReTiHA) and Parent monitoring for people with their family living abroad.
TL;DR: The review primarily focuses on the recently used wireless data acquisition system and execution of AI resources for data prediction and data diagnosis in RCC buildings and bridges and indicates the lag in real-world execution of structural health monitoring technologies despite advances in academia.
Abstract: Structural Health Monitoring is gaining popularity in recent times because of advancements in technology and the increasing need for repair and rehabilitation. The shift from conventional wired technologies to advanced wireless technologies is also gradually increasing in the past decade. These sensor networks are economical when used for monitoring huge structures with high design life and safety requirements like highway and roadway bridges, multi-story buildings, chimneys, offshore platforms, and nuclear reactors. Smart sensors when paired along with Artificial Intelligence tools like Artificial Neural Networks, Machine Learning, Deep Learning, and its derivatives Convolutional Neural Networks, Hybrid Intelligence, Cloud Computing make the monitoring system completely automated. This paper is a comprehensive review of advances in data acquisition, processing, diagnosis, and retrieval stages of Structural Health Monitoring both academically and commercially. The review primarily focuses on the recently used wireless data acquisition system and execution of AI resources for data prediction and data diagnosis in RCC buildings and bridges. The review also indicates the lag in real-world execution of structural health monitoring technologies despite advances in academia and insists on the development of standards to gel the gap.