Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.

Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

Structural Health Monitoring Framework Based on Internet of Things: A Survey

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications.

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).

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

Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.

https://www.mdpi.com/1424-8220/17/6/1457/pdf

Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

Wireless sensor networks (WSNs) are one of the most able technologies in the structural health monitoring (SHM) field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

/pdf/investigation-of-wireless-sensor-networks-for-structural-41phvlunuc.pdf

Investigation of Wireless Sensor Networks for Structural Health Monitoring

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.

https://downloads.hindawi.com/journals/js/2015/139695.pdf

Structural Health Monitoring of Wind Turbine Blades: Acoustic Source Localization Using Wireless Sensor Networks

Investigation of sampling frequency requirements for acoustic source localisation using wireless sensor networks

High sampling frequencies in acoustic wireless sensor network (AWSN) are required to achieve precise sound localisation. But they are also mean analysis time and memory intensive (i.e., huge data to be processed and more memory space to be occupied which form a burden on the nodes limited resources). Decreasing sampling rates below Nyquist criterion in acoustic source localisation (ASL) applications requires development of the existing time delay estimation techniques in order to overcome the challenge of low time resolution. This work proposes using envelope and wavelet transform to enhance the resolution of the received signals through the combination of different time-frequency contents. Enhanced signals are processed using cross-correlation in conjunction with a parabolic fit interpolation to calculate the time delay accurately. Experimental results show that using this technique, estimation accuracy was improved by almost a factor of 5 in the case of using 4.8 kHz sampling rate. Such a conclusion is useful for developing precise ASL without the need of any excessive sensor resources, particularly for structural health monitoring applications.

/pdf/envelope-and-wavelet-transform-for-sound-localisation-at-low-3gcv1h0543.pdf

Envelope and Wavelet Transform for Sound Localisation at Low Sampling Rates in Wireless Sensor Networks

Integration of acoustic wireless technology in structural health monitoring (SHM) applications introduces new challenges due to requirements of high sampling rates, additional communication bandwidth, memory space, and power resources. In order to circumvent these challenges, this chapter proposes a novel solution through building a wireless SHM technique in conjunction with acoustic emission (AE) with field deployment on the structure of a wind turbine. This solution requires a low sampling rate which is lower than the Nyquist rate. In addition, features extracted from aliased AE signals instead of reconstructing the original signals on-board the wireless nodes are exploited to monitor AE events, such as wind, rain, strong hail, and bird strike in different environmental conditions in conjunction with artificial AE sources. Time feature extraction algorithm, in addition to the principal component analysis (PCA) method, is used to extract and classify the relevant information, which in turn is used to classify or recognise a testing condition that is represented by the response signals. This proposed novel technique yields a significant data reduction during the monitoring process of wind turbine blades.

Omar Mabrok Bouzid

Papers

Investigation of Wireless Sensor Networks for Structural Health Monitoring

Structural Health Monitoring of Wind Turbine Blades: Acoustic Source Localization Using Wireless Sensor Networks

Investigation of sampling frequency requirements for acoustic source localisation using wireless sensor networks

Envelope and Wavelet Transform for Sound Localisation at Low Sampling Rates in Wireless Sensor Networks

Wireless AE Event and Environmental Monitoring for Wind Turbine Blades at Low Sampling Rates