Structural Health Monitoring Technology Research Articles

Fibre Bragg Grating Based Acoustic Emission Measurement System for Structural Health Monitoring Applications.

Fiber Bragg grating (FBG)-based acoustic emission (AE) detection and monitoring is considered as a potential and emerging technology for structural health monitoring (SHM) applications. In this paper, an overview of the FBG-based AE monitoring system is presented, and various technologies and methods used for FBG AE interrogation systems are reviewed and discussed. Various commercial FBG AE sensing systems, SHM applications of FBG AE monitoring, and market potential and recent trends are also discussed.

Development of an integrated sacrificial sensor for damage detection and monitoring in composite materials and adhesively bonded joints

Quality assurance of adhesively bonded joints is of vital importance if their benefits are to be exploited across a wide range of industrial applications. A novel lightweight, low-cost, non-invasive embedded sacrificial sensor is proposed, capable of detecting damage within an adhesively bonded joint, which could also be used in a laminated composite structure. The sensor operation uses changes in electrical resistance, increasing as the sensing material area diminishes with damage progression. Initial tests prove the sensor concept by showing that the electrical resistance of the sensor increases proportionally with material removal, mimicking the sensor operation. Thermography is used to verify the current flow through the sensor and that any localised heating caused by the sensor is minimal. Short beam interlaminar shear strength (ILSS) tests show that embedding sensors in a composite laminates did not cause a reduction in material interfacial structural performance. Finally, the in situ performance of the sensor is demonstrated in quasi-static tensile tests to failure of adhesively bonded single lap joints (SLJs) with sensors embedded in the bond line. Prior to crack initiation, an electrical response occurs that correlates with increasing applied load, suggesting scope for secondary uses of the sensor for load monitoring and cycle counting. Crack initiation is accompanied by a rapid increase in electrical resistance, providing an indication of failure ahead of crack propagation and an opportunity for timely repair. As the crack damage propagated, the electrical response of the sensor increased proportionally. The effect of the sensor on the overall structural performance was assessed by comparing the failure load of joints with and without the embedded sensor with no measurable difference in ultimate strength. The research presented in the article serves as an important first step in developing a simple yet promising new technology for structural health monitoring with numerous potential applications.

Research Progress of Structural Health Monitoring Based on BIM Technology

Structural health monitoring technology is one of the effective means to ensure the reliability of the structure, it is of great significance to the subsequent operation and maintenance of the structure to detect and identify the deterioration and damage of the structure during the service period, and realize the early warning and dynamic evaluation of the structural safety. Under the background of the rapid development of computer and other information technology, BIM technology is increasingly used in the field of structural health monitoring because of its good coordination, simulation, optimization, visualization, and graphing characteristics. This paper expounds and analyzes the research and application of structural health monitoring technology, BIM technology and structural health monitoring system based on BIM technology, and points out the technical and application problems that need to be solved urgently in the development of this field.

A SVD-Based Signal De-Noising Method With Fitting Threshold for EMAT

The electromagnetic acoustic transducer (EMAT) is a powerful and useful non-destructive testing technology for structural health monitoring. However, EMAT has an issue of low efficiency in conversion and its signal is easily affected by noise, which make it difficult to accurately identify and evaluate structural defects. Thereby, signal de-noising preprocessing is essential for the evaluation of defects. In this paper, we proposed an improved singular value decomposition (SVD) de-noising method based on the fitting threshold for EMAT signal. For SVD de-noising method, the key point is to determine the singular value threshold for reconstructing the signal. We applied a segmented regression model to find the appropriate threshold in this approach. To investigate the efficacy of the proposed method, simulation signals and experimental signals are used for verification respectively. A comparative analysis has been under-taken to confirm that the proposed signal de-noising has advantages over other methods in EMAT signal noise reduction, and it is useful for more accurate evaluation of defects.

A Study on Technological Dynamics in Structural Health Monitoring Using Intelligent Fault Diagnosis Techniques: A Patent-Based Approach

The performance and reliability of structural components are greatly influenced by the presence of any abnormality in them. To this purpose, structural health monitoring (SHM) is recognized as a necessary tool to ensure the safety precautions and efficiency of both mechanical and civil infrastructures. Till now, most of the previous work has emphasized the functioning of several SHM techniques and systematic changes in SHM execution. However, there exist insufficient data in the literature regarding the patent-based technological developments in the SHM research domain which might be a useful source of detailed information for worldwide research institutes. To address this research gap, a method based on the Co-Operative Patent Classification (CPC) codes is proposed in the current study. The proposed method includes the patent analysis of SHM in terms of its global publication trend and technology-based applications. This analysis is performed using patent database search tools, namely, IncoPat and Espacenet. The period ranging from 2005 to 2019 is selected to retrieve the required patent documents. A new approach termed as Patents’ value is utilized to investigate the technological impact of a patent in the form of forward citations, technical stability, and scope of protection. The identification of emerging SHM techniques and forecasting of vacant technology is also part of current research work. The research results have revealed the increasing trend in the number of published patents each year related to various SHM technologies. In this regard, China, the United States, and South Korea are notified as to the major depositor countries, respectively. Hence, mapping of patent data in this research is an effort to illustrate the effectiveness of the proposed method to demonstrate the development trends and dynamic inventions over the time in SHM research domain to achieve the optimal damage inspections of various mechanical components.

Smart structural health monitoring in civil engineering: A survey

Structural health monitoring (SHM) technologies for tracking and assessing socially and historically constructed long-haul spans was being increasingly recognized, and several government officials have realized the importance of the methodology. The article reviews recent advance of the SHM technologies in order to long-span bridges. A SHM framework is implemented to deploy. The research design and quality evaluation involving modal recognition techniques, image processing approaches, and hazard detection systems were then checked and presented. This research incorporates a developing capacity tool in combination with a low- and mid-index demonstrable evaluation tool for bridge architectural condition monitoring. The strength of concrete, stiffness of the steel beam surface, density of the top surface of the hollow plate, crack propagation, cover hardness of metal bars etc.From the examples, the open issues and recent developments for a SHM system have been addressed.

Structural health monitoring through surface acoustic wave inspection deployed on capillaries embedded in additively manufactured components

Surface Acoustic Wave inspection is a well-known non-destructive testing technique that receives considerable attention to become implemented as a Structural Health Monitoring system. The current work presents a novel approach to embed Surface Acoustic Wave-based Structural Health Monitoring technology inside additively manufactured components. A capillary network is to be integrated inside the component and Surface Acoustic Wave inspection is then deployed on the free capillary surface during the component’s operation to warn upcoming failures.

Damage detection in operational wind turbine blades using a new approach based on machine learning

The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.

Chaotic search algorithm for detection of discontinuities using guided waves and beamforming data

The goal of inspection and continuous health monitoring of engineering structures is to detect defects particularly during their early stages of development. In this work, a method using an optimization scheme for localization of a target using ultrasonic guided waves in a thin plate and data sampled from a beamforming is studied. The proposed optimization scheme finds the target by using a search algorithm based on random and chaotic walk search to reduce the number of evaluation points within the search space. The data used for the scheme is extracted from the using only a reduced number of evaluation points from the energy reconstruction map of a delay and sum beamforming algorithm. The proposed approach is numerically studied and implemented experimentally for discontinuity detection in a plate using guided Lamb waves. The combined experimental and numerical analysis indicates that the searching algorithm was able to find an acoustic target generated by a discontinuity in a plate even when the signals were contaminated by noise.

A Brief Review on Structural Health Monitoring Sensor Technology in Civil and Aviation Technology Applications

The Structural Health Monitoring (SHM) system is a method for evaluating and monitoring the integrity of the structure. It has been widely used in various engineering sectors, such as in aerospace, civil and energy sectors due to its ability to react to structural changes on an online basis or in real-time monitoring. The SHM system was able to evolve and work in a variety of structures or components due to the rapid development of sensor technology. It is believed that SHM will become an important tool in various industries for structural monitoring in future years. The paper presents a summary of the latest SHM technology in civil and aviation technology applications as well as the challenges in cost analysis and certification issues for the implementation of SHM.

Energy Harvesting Technologies for Structural Health Monitoring of Airplane Components-A Review.

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.

A Brief Review on Structural Health Monitoring Sensor Technologyin Civil and Aviation Technology Applications

The Structural Health Monitoring (SHM) system is a method for evaluating and monitoring the integrity of the structure. It has been widely used in various engineering sectors, such as in aerospace, civil and energy sectors due to its ability to react to structural changes on an online basis or in real-time monitoring. The SHM system was able to evolve and work in a variety of structures or components due to the rapid development of sensor technology. It is believed that SHM will become an important tool in various industries for structural monitoring in future years. The paper presents a summary of the latest SHM technology in civil and aviation technology applications as well as the challenges in cost analysis and certification issues for the implementationof SHM.

A Brief Review on Structural Health Monitoring Sensor Technology in Civil and Aviation Technology Applications

The Structural Health Monitoring (SHM) system is a method for evaluating and monitoring the integrity of the structure. It has been widely used in various engineering sectors, such as in aerospace, civil and energy sectors due to its ability to react to structural changes on an online basis or in real-time monitoring. The SHM system was able to evolve and work in a variety of structures or components due to the rapid development of sensor technology. It is believed that SHM will become an important tool in various industries for structural monitoring in future years. The paper presents a summary of the latest SHM technology in civil and aviation technology applications as well as the challenges in cost analysis and certification issues for the implementation of SHM.

Social group optimization algorithm for civil engineering structural health monitoring

Social group optimization (SGO) is a human-based metaheuristic optimization technique which shows accurate results for different benchmark functions but has not been studied for civil engineering structural health monitoring problems. This article deals with the use of SGO for damage analysis of different modelled civil engineering structures and a real-life American Society of Civil Engineers (ASCE) benchmark structure using a stiffness-based objective function. It is observed that SGO is not able to identify the damage in the structures owing to the algorithm becoming trapped in local optima. To improve the performance of SGO, a modified social group optimization (MSGO) is proposed, which deals with the drawbacks of SGO in dealing with the complicated objective functions of civil structures. It is observed that MSGO shows accurate damage detection capability, with errors of less than 1%, in the civil structures considered, even in the presence of noise.

Design of a Large-Scale Piezoelectric Transducer Network Layer and Its Reliability Verification for Space Structures.

As an effective structural health monitoring (SHM) technology, the piezoelectric transducer (PZT) and guided wave-based monitoring methods have attracted growing interest in the space field. When facing the large-scale monitoring requirements of space structures, a lot of PZTs are needed and may cause problems regarding to additional weight of connection cables, placement efficiency and performance consistency. The PZT layer is a promising solution against these problems. However, the current PZT layers still face challenges from large-scale lightweight monitoring and the lack of reliability assessment under extreme space service conditions. In this paper, a large-scale PZT network layer (LPNL) design method is proposed to overcome these challenges, by adopting a large-scale lightweight PZT network design method and network splitting–recombination based integration strategy. The developed LPNL offers the advantages of being large size, lightweight, ultra-thin, flexible, customized in shape and highly reliable. A series of extreme environmental tests are performed to verify the reliability of the developed LPNL under space service environment, including extreme temperature conditions, vibration at different flying phases, landing impact, and flying overload. Results show that the developed LPNL can withstand these harsh environmental conditions and presents high reliability and functionality.

Research on an Improved Metal Surface Defect Detection Sensor Based on a 3D RFID Tag Antenna

Structural health monitoring (SHM) technology is a monitoring process and early warning method for the health status or damage of special workpiece structures by deploying sensors. In recent years, there have been many studies on SHM, such as ultrasonic, pulsed eddy current, optical fiber, magnetic powder, and other nondestructive testing technologies. Due to their sensor deployment, testing environment, power supply, and transmission line wiring mechanism, they bring problems such as detection efficiency, long-term monitoring, and unreliable systems. The combination of wireless sensing technology and intelligent detection technology is used to solve the above problems. Therefore, this paper studies the tag antenna smart sensor, which is used to characterize the extension of metal defects in SHM. Then, it presents a wireless passive three-dimensional sensing antenna, and simulations verify the feasibility of the antenna. The simulation results show that the antenna can characterize the two extension directions of depth and width of the metal surface structure smooth defect. At the same time, the antenna can characterize the position of smooth defects on the surface of metal structures relative to the antenna and then realize the smooth defect positioning.

Fully Integrated Mechanoluminescent Devices with Nanometer-Thick Perovskite Film as Self-Powered Flexible Sensor for Dynamic Pressure Sensing

Flexible pressure sensors have numerous future applications in artificial electronic skins, foot ulcer detection, structural health monitoring, soft robotics, and wearable technology. We, herein, report an efficient, low-cost manufacturing technique to construct a compact, sensitive, robust yet flexible pressure sensor by fully integrating the mechanoluminescent device with nanometer thick organometal halide perovskite. The mechanoluminescent-perovskite flexible sensor does not require any power source at the sensing location. The sensor was subjected to repeated pressure and generated a distinct electrical current signal corresponding to each pressure cycle. The current intensity increases linearly with the increase of applied pressure on the sensor, which is desirable. Our sensor can detect pressure as low as 11 kPa with a large sensing range and a sensitivity of 0.095 kPa–1. The sensor displays consistent signals over 1000 cycles, which demonstrates its potential as a reliable sensor in practical applications such as structural health monitoring. Our approach of efficiently harvesting mechanoluminescent light via integrated light-absorbing nanometer thick perovskite might open a window for advanced research in self-powered mechanoluminescent-perovskite-based devices.

Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

Abstract This paper investigates the damage localization in a pressure vessel using guided wave-based structural health monitoring (SHM) technology. An online SHM system was developed to automatically select the guided wave propagating path and collect the generated signals during the monitoring process. Deep learning approach was employed to train the convolutional neural network (CNN) model by the guided wave datasets. Two piezo-electric ceramic transducers (PZT) arrays were designed to verify the anti-interference ability and robustness of the CNN model. Results indicate that the CNN model with seven convolution layers, three pooling layers, one fully connected layer, and one Softmax layer could locate the damage with 100% accuracy rate without overfitting. This method has good anti-interference ability in vibration or PZTs failure condition, and the anti-interference ability increases with increasing of PZT numbers. The trained CNN model can locate damage with high accuracy, and it has great potential to be applied in damage localization of pressure vessels.

Strain Data Analysis of Small and Medium Bridge Structures Based on Finite Element

Structural health monitoring technology is increasingly used in the operation and management of small and medium span bridge structures, which also brings the problem that it is difficult to fully interpret and utilize the mass monitoring data. In this paper, the Sanyang Bridge is used as the engineering background, based on the strain monitoring data, combined with the finite element calculation software MIDAS/civil, to evaluate and analyze the operating state of the bridge structure. The results show that the overall operation status of Sanyang Bridge is good, the evaluation method combining finite element calculation and monitoring data is reasonable, this method can be applied to data processing and condition assessment of small and medium span bridges.

Structural health evaluation of the prestressed concrete using advanced acoustic emission (AE) parameters

Acoustic emission (AE) is a promising technology for structural health monitoring to reduce costs and improve performance but hard to interpret. This paper puts forward an advanced deep neural network (DNN) method to dig information between AE parameters and performance of the structures since raw data obtained by the AE structural monitoring system is nearly useless without reinterpretation. Firstly, this study carried out a series of AE monitoring to catch crack signals of the prestressed concrete samples of cycle loads under three-point bending tests. The AE parameters analyzed include Amean, RA, LE, LQ, Amax, EA, Ve, and AK. The combination of them gives a comprehensive structural performance evaluation by the DNN model. The results show a good correlation between AE parameters with the crack activities of different loads. Amean, RA, LE are the critical parameters in detecting whether the structure meets the design strength requirement. The combination of all these parameters gives an overall assessment of the structures. Finally, the method applies to the monitoring of two real-world bridges and gets comprehensive evaluations for all the monitoring points. This paper not only addresses a systematic experiment and a comprehensive parameter analysis method to realize data interpretation, but also practical applications in real-world bridges.