Structural Health Monitoring Platform Research Articles

IoT platform for offshore wind turbine blade structure health monitoring

Wind energy, as renewable energy, is critical in targeting carbon neutrality or net zero emissions in order to address global climate change. Compared with onshore wind turbines, offshore wind turbines enjoy generally higher wind speed, thus producing more electric energy. However, the harsh marine environment, including high winds, wave-induced vibrations and and sea and rain corrosion and erosion, can lead to structural damage, reduced operational efficiency and increased maintenance cost. This paper presents a novel Internet of Things (IoT) platform for structural health monitoring (SHM) of the offshore wind turbine’s key component, the wind turbine blades. This research focuses on developing a comprehensive, real-time monitoring system that utilises advanced sensor networks, edge computing, and advanced predictive algorithms to strengthen on-time maintenance of turbine blades, while avoiding unnecessary and costly regular maintenance.

Signal Processing Methodology of Response Data from a Historical Arch Bridge toward Reliable Modal Identification

The paper is part of a case study concerning the structural assessment of a historical infrastructure in the local territory, a road three-span reinforced concrete arch bridge over a river, built by the end of World War I (1917). The purpose of the paper is twofold: first, in-situ acquired response data are systematically analysed by specific signal processing techniques, to form a devoted methodological procedure and to extract useful information toward possible interpretation of the current structural conditions; second, the deciphered information is elaborated, in view of obtaining peculiar conceptualisations of detailed features of the structural response, as meant to achieve quantitative descriptions and modelling, for final Structural Health Monitoring (SHM) and intervention purposes. The proposed methodology, integrating self-implemented and adapted classical signal processing methods, and refined techniques, such as Wavelet analysis and ARMA models, assembles a rather general, systematic methodological approach to signal processing, highlighting the capability to extract useful and fundamental information from acquired response data, also endowed of a non-stationary character, toward final structural interpretation, identification and modelling, thus enabling for developing a reliable and effective SHM platform, on strategic ageing infrastructures. For the present case study, non-stationary characteristics of the response signals are revealed and flattened out, to identify the underlying fundamental frequencies of the infrastructure and to advance particular interpretations of its current structural behaviour, in forming an enlarging structural consciousness of the bridge at hand.

Development of a Wireless Corrosion Detection System for Steel-Framed Structures Using Pulsed Eddy Currents

Structural health monitoring (SHM) can be more efficient with the application of a wireless sensor network (WSN). However, the hardware that makes up this system should have sufficient performance to sample the data collected from the sensor in real-time situations. High-performance hardware can be used for this purpose, but is not suitable in this application because of its relatively high power consumption, high cost, large size, and so on. In this paper, an optimal remote monitoring system platform for SHM is proposed based on pulsed eddy current (PEC) that is utilized for measuring the corrosion of a steel-framed construction. A circuit to delay the PEC response based on the resistance–inductance–capacitance (RLC) combination was designed for data sampling to utilize the conventional hardware of WSN for SHM, and this approach was verified by simulations and experiments. Especially, the importance of configuring sensing modules and the WSN for remote monitoring were studied, and the PEC responses caused by the corrosion of a specimen made with steel were able to be sampled remotely using the proposed system. Therefore, we present a remote SHM system platform for diagnosing the corrosion condition of a building with a steel structure, and proving its viability with experiments.

Health assessment based on dynamic characteristics of reinforced concrete beam using realtime wireless structural health monitoring sensor

ABSTRACT The uses of dynamic estimations on existing structures are numerous: evaluation of their seismic vulnerability, structure’s abilities in post-quake circumstances or after changes in the region, and so on. At present, this sort of estimation enables auxiliary conclusion on a global scale, while the identification and the assessment of local damage (each element of the structure) needs to be investigated. Additionally, we begin to co-exist and interact with smart interconnected devices known as the Internet of Things (IoT). The IoT brought new opportunities for our society. Yet, most of the existing Structural Health Monitoring (SHM) systems are not connected to the IoT. Hence, this article studies the integration of complete real-time SHM platform with the Iot system, which can provide structural health of a structure and the performance of this sensor was check on a reinforced concrete (RC) beam. The RC beam was loaded in the central part in several stages corresponding to different damage states. Displacements were measured simultaneously using displacement sensors. After each load/unload cycle, dynamic measurements were taken using accelerometers. The perceptions from investigation were displayed, with the presence of damage and the reduction in natural frequencies that occurred in conjunction with reduction in stiffness.

Numerical and experimental study of aircraft structural health

The purpose of this work is to monitor the structural health of the Portuguese Air Force (PoAF) EPSILON TB30 fleet by measuring the dynamic behaviour of aircraft’s critical components towards the optimization of maintenance programme scheduling. The mission load spectrum and the structural response are simultaneously measured using a combination of accelerometers and strain-gauges, enabling the calculation of individual transference functions for each critical component monitored. The EPSILON TB30 fleet was divided into two different types of aircrafts: a) IMA-Highly Instrumented Aircrafts which are able to capture vertical accelerations and strain at critical locations and b) LIA-Less Instrumented Aircrafts that make use of the transference functions for mapping the captured acceleration data to stress at pre-selected structural hotspots. The fleet operation is continuously analysed to calculate the fatigue damage of each component and predict the operation limit for each aircraft. In this context, finite element models of critical components are used to define the critical locations of sensors used for monitoring stress in those locations. The maximum principal stress zones of the 2nd bulkhead beam was obtained and are consistent with the manufacturer’s reports that place fatigue crack initiation near the bottom filleted joint of the innermost rib, during real scale fatigue test. Mission is analysed using the structural health monitoring platform PRODDIA™ Aero which uses time and frequency-based fatigue models to predict the structural health of the entire fleet.

Internet of Things (IoT) Platform for Structure Health Monitoring

Increase in the demand for reliable structural health information led to the development of Structural Health Monitoring (SHM). Prediction of upcoming accidents and estimation of useful life span of a structure are facilitated through SHM. While data sensing is the core of any SHM, tracking the data anytime anywhere is a prevailing challenge. With the advancement in information technology, the concept of Internet of Things (IoT) has made it possible to integrate SHM with Internet to track data anytime anywhere. In this paper, a SHM platform embedded with IoT is proposed to detect the size and location of damage in structures. The proposed platform consists of a Wi-Fi module, a Raspberry Pi, an Analog to Digital Converter (ADC), a Digital to Analog Converter (DAC), a buffer, and piezoelectric (PZT) sensors. The piezoelectric sensors are mounted as a pair in the structure. Data collected from the piezoelectric sensors will be used to detect the size and location of damage using a proposed mathematical model. Implemented on a Raspberry Pi, the proposed mathematical model will estimate the size and location of structural damage, if any, and upload the data to Internet. This data will be stored and can be checked remotely from any mobile device. The system has been validated using a real test bed in the lab.

Active-sensing platform for structural health monitoring: Development and deployment

Embedded sensing for structural health monitoring is a rapidly expanding field, propelled by algorithmic advances in structural health monitoring and the ever-shrinking size and cost of electronic hardware necessary for its implementation. Although commercial systems are available to perform the relevant tasks, they are usually bulky and/or expensive because of their high degree of general utility to a wider range of applications. As a result, multiple separate devices may be required in order to obtain the same results that could be obtained with a structural health monitoring–specific device. This work presents the development and deployment of a versatile, Wireless Active-Sensing Platform, designed for the particular needs of embedded sensing for multi-scale structural health monitoring. The Wireless Active-Sensing Platform combines a conventional data acquisition ability to record voltage output (e.g. from strain or acceleration transducers) with ultrasonic guided wave-based active-sensing, and a seamlessly integrated impedance measurement mode, enabling impedance-based structural health monitoring and piezoelectric sensor diagnostics to reduce the potential for false positives in damage identification. The motivation, capabilities, and hardware design for the Wireless Active-Sensing Platform are reviewed, and three deployment examples are presented, each demonstrating an important aspect of embedded sensing for structural health monitoring.

Citizen Sensors for SHM: Towards a Crowdsourcing Platform.

This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. The ubiquity of smartphones has provided an opportunity to create low-cost sensor networks for SHM. Crowdsourcing has given rise to citizen initiatives becoming a vast source of inexpensive, valuable but heterogeneous data. Previously, the authors have investigated the reliability of smartphone accelerometers for vibration-based SHM. This paper takes a step further to integrate mobile sensing and web-based computing for a prospective crowdsourcing-based SHM platform. An iOS application was developed to enable citizens to measure structural vibration and upload the data to a server with smartphones. A web-based platform was developed to collect and process the data automatically and store the processed data, such as modal properties of the structure, for long-term SHM purposes. Finally, the integrated mobile and web-based platforms were tested to collect the low-amplitude ambient vibration data of a bridge structure. Possible sources of uncertainties related to citizens were investigated, including the phone location, coupling conditions, and sampling duration. The field test results showed that the vibration data acquired by smartphones operated by citizens without expertise are useful for identifying structural modal properties with high accuracy. This platform can be further developed into an automated, smart, sustainable, cost-free system for long-term monitoring of structural integrity of spatially distributed urban infrastructure. Citizen Sensors for SHM will be a novel participatory sensing platform in the way that it offers hybrid solutions to transitional crowdsourcing parameters.

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

A SMART Platform is developed based on sensor readings for Structural Health Monitoring of a stiffened composite panel. The platforms main function is divided into three categories: Passive sensing, Active sensing and Optimal sensor positioning. The platform has self-diagnostic capabilities, i.e. prior to its application the health of the sensors and their connection will be checked to avoid any false alarm. Passive sensing results in impact location and force magnitude detection. Active sensing is performed for damage detection. It results in detecting the damage location and severity. Finally the optimal sensor location can be provided given the number of sensors and probability of detection value. This platform is the first step in applying the developed SHM methodologies to real size structures in service load conditions.

Load monitoring of aerospace structures utilizing micro-electro-mechanical systems for static and quasi-static loading conditions

The National Research Council Canada (NRC) has worked on the development of structural health monitoring (SHM) test platforms for assessing the performance of sensor systems for load monitoring applications. The first SHM platform consists of a 5.5 m cantilever aluminum beam that provides an optimal scenario for evaluating the ability of a load monitoring system to measure bending, torsion and shear loads. The second SHM platform contains an added level of structural complexity, by consisting of aluminum skins with bonded/riveted stringers, typical of an aircraft lower wing structure. These two load monitoring platforms are well characterized and documented, providing loading conditions similar to those encountered during service. In this study, a micro-electro-mechanical system (MEMS) for acquiring data from triads of gyroscopes, accelerometers and magnetometers is described. The system was used to compute changes in angles at discrete stations along the platforms. The angles obtained from the MEMS were used to compute a second, third or fourth order degree polynomial surface from which displacements at every point could be computed. The use of a new Kalman filter was evaluated for angle estimation, from which displacements in the structure were computed. The outputs of the newly developed algorithms were then compared to the displacements obtained from the linear variable displacement transducers connected to the platforms. The displacement curves were subsequently post-processed either analytically, or with the help of a finite element model of the structure, to estimate strains and loads. The estimated strains were compared with baseline strain gauge instrumentation installed on the platforms. This new approach for load monitoring was able to provide accurate estimates of applied strains and shear loads.

A holistic approach to decentralized structural damage localization using wireless sensor networks

Wireless sensor networks (WSNs) have become an increasingly compelling platform for Structural Health Monitoring (SHM) applications, since they can be installed relatively inexpensively onto existing infrastructure. Existing approaches to SHM in WSNs typically address computing system issues or structural engineering techniques, but not both in conjunction. In this paper, we propose a holistic approach to SHM that integrates a decentralized computing architecture with the Damage Localization Assurance Criterion algorithm. In contrast to centralized approaches that require transporting large amounts of sensor data to a base station, our system pushes the execution of portions of the damage localization algorithm onto the sensor nodes, reducing communication costs by two orders of magnitude in exchange for moderate additional processing on each sensor. We present a prototype implementation of this system built using the TinyOS operating system running on the Intel Imote2 sensor network platform. Experiments conducted using two different physical structures demonstrate our system’s ability to accurately localize structural damage. We also demonstrate that our decentralized approach reduces latency by 65.5% and energy consumption by 64.0% compared to a typical centralized solution.

Implementation of a bio-inspired two-mode structural health monitoring system

A bio-inspired two-mode structural health monitoring (SHM) system based on the Naive Bayes(NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

Platform for structural health monitoring of buildings utilizing smart sensors and advanced diagnosis tools

A structural health monitoring (SHM) system monitors the condition of structures such as tall buildings and promptly and quantitatively evaluates their deterioration and damage due to natural hazards and aging. The system is under keen attention for prolonging the structures' lives and increasing their durability while keeping them in healthy condition. Several SHM systems have been proposed and tested for experiments or for real structures. However, most of these systems are customized to particular structures, so that they are not sufficiently scalable. Moreover, there is no standard data format for the data they gather. Thus, it is difficult to treat the data stochastically. We have been developing smart sensor units with data acquisition and data management capability. Many data analysis tools utilizing system identification and an easy-to-use user interface for SHM have been also studied. The SHM platform consists of these tools to evaluate the soundness of building structures, the server system to publish the diagnosis results to the users, and the smart sensor units for automatic data management. Moreover, as it has a flexible data model and an automatic data entry system for acquired data, the system can be applied to any building structures for treating them from a stochastic viewpoint. In addition, the smart sensor unit we developed shortens the configuration process and enables us to control the measurement configuration through a web interface. This study presents the key features of the SHM platform. Copyright © 2010 John Wiley & Sons, Ltd.

A Wireless Sensor Network Platform for Structural Health Monitoring: enabling accurate and synchronized measurements through COTS+custom-based design

Structural health monitoring has long been identified as a prominent application of Wireless Sensor Networks (WSNs), as traditional wired-based solutions present some inherent limitations such as installation/maintenance cost, scalability and visual impact. Nevertheless, there is a lack of ready-to-use and off-the-shelf WSN technologies that are able to fulfill some most demanding requirements of these applications, which can span from critical physical infrastructures (e.g. bridges, tunnels, mines, energy grid) to historical buildings or even industrial machinery and vehicles. Low-power and low-cost yet extremely sensitive and accurate accelerometer and signal acquisition hardware and stringent time synchronization of all sensors data are just examples of the requirements imposed by most of these applications. This paper presents a prototype system for health monitoring of civil engineering structures that has been jointly conceived by a team of civil, and electrical and computer engineers. It merges the benefits of standard and off-the-shelf (COTS) hardware and communication technologies with a minimum set of custom-designed signal acquisition hardware that is mandatory to fulfill all application requirements.

Versatile robotic platform for structural health monitoring and surveillance

Utilizing robotic based reconfigurable nodal structural health monitoring systems has many advantages over static or human positioned sensor systems. However, creating a robot capable of traversing a variety of civil infrastructures is a difficult task, as these structures each have unique features and characteristics posing a variety of challenges to the robot design. This paper outlines the design and implementation of a novel robotic platform for deployment on ferromagnetic structures as an enabling structural health monitoring technology. The key feature of this design is the utilization of an attachment device which is an advancement of the common magnetic base found in the machine tool industry. By mechanizing this switchable magnetic circuit and redesigning it for light weight and compactness, it becomes an extremely efficient and robust means of attachment for use in various robotic and structural health monitoring applications. The ability to engage and disengage the magnet as needed, the very low power required to do so, the variety of applicable geometric configurations, and the ability to hold indefinitely once engaged make this device ideally suited for numerous robotic and distributed sensor network applications. Presented here are examples of the mechanized variable force magnets, as well as a prototype robot which has been successfully deployed on a large construction site. Also presented are other applications and future directions of this technology.