Sensor Networks For Structural Health Monitoring Research Articles

Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade

Structural Health Monitoring Systems (SHMS) are currently widely investigated in the literature, however, their application to the aerospace industry is still limited. One of the reasons lies in the lack of methods aimed at evaluating their economic impact on the lifecycle of the structural element to be monitored [1]. In this work the economic impact of FBG-based SHMS was evaluated on a composite helicopter rotor blade, considering two perspectives: Beginning Of Life (BOL), concerning the blade production, and Middle Of Life (MOL), concerning its usage. The processes were modeled using the IDEF0 methodology, which allowed deriving the cost models for two scenarios: the current one, and the one including the SHMS. In BOL, the SHMS was supposed providing support to the development of new composite rotor blades, including the curing cycle definition and certification tests. In MOL the SHMS was supposed performing automatic scheduled inspections coupled with the traditional ones. The cost model was implemented in the Probabilistic Damage Tolerance Analysis [2] to compare the scenarios having the same blade Probability Of Failure and considering the performances of the SHMS in terms of Probability Of Detection and Probability of False Alarm. Results show that an economic benefit may be achieved using the SHMS in the development of new blades, potentially reducing the number of blades tested and number of autoclave cycles. Moreover, it was found that the traditional scheduled detailed inspection intervals could be extended if automatic SHMS inspections are performed in addition, maintaining the same Probability Of Failure. [1] Büchter, K. D. et al. (2022). Modeling of an aircraft structural health monitoring sensor network for operational impact assessment. Structural Health Monitoring, 21(1), 208-224. [2] Lin, K. Y., & Styuart, A. V. (2007). Probabilistic approach to damage tolerance design of aircraft composite structures. Journal of Aircraft, 44(4), 1309-1317.

Validation of an Abaqus impact wave propagation model

Structural Health Monitoring (SHM) is an important topic in aircraft industry to reduce the maintenance costs and increase the availability of an aircraft fleet through automating the determination of the health of the aircraft structure and systems using on-site sensors, like piezoelectric (PZT) and optic fibre Bragg grating (FBG) sensors that are often applied in SHM sensor networks. A finite element model for wave propagation due to an impact is very desirable to enable the design of optimal sensor network configurations for SHM applications, to obtain a better understanding of wave propagation (especially in composite structures) and also to generate simulated (virtual) test data for SHM algorithm development. Therefore, an Abaqus explicit finite element (FE) model was developed for wave propagation due to impacts. To validate such a model, a series of impact tests were performed with a drop tower on an aluminium clamped square panel, at different impact energy levels and different impact locations. On the panel, eight FBG and six PZT sensors were installed. These experimental results were used to validate the FE model, in which the impacts were simulated and the computed PZT and FBG sensor responses were compared against the experimental results. The paper will present the impact test setup, the finite element model and the validation results. The main focus of the paper will be the sensitivity analysis, identifying the model parameters that significantly affect the impact sensor responses and their optimal settings. A very good correlation between the numerical and experimental results was obtained. In near future work, the model will be extended to a (thermoplastic) composite panel, for which experimental results on a flat composite panel already have been collected, and a stiffened composite panel representative of an actual horizontal tail plane are planned.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

Switch-Beam Antenna Techniques for Bridge Structural Health Monitoring With GaAs-Based Solar Energy Harvesting

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The structural health monitoring (SHM) of bridges with wireless sensor networks (WSNs) is addressed by leveraging two distinct but interrelated aspects: GaAs-based solar energy harvesting and switched-beam antenna strategies in combination with asynchronous media access control (MAC) protocols. The joint optimization of solar energy harvesting and switched-beam directional antennas at the nodes is considered and compared with an equivalent omnidirectional antenna network. To address the limited energy budget in battery-based sensor nodes which is a serious limitation in long-lived applications such as bridge SHM, an efficient solar harvesting solution is put forth based on the good performance of GaAs cells even under low-light conditions. Given the current state of the art in GaAs cells, single-junction cells were selected due to the cost of double-or triple-junction versions. The simulation model includes the residual energy capacity with GaAs-based solar energy harvesting of actual selected components (microcontroller, radio interface chip). The model was implemented on top of the Omnet<inline-formula> <tex-math notation="LaTeX">$++$</tex-math> </inline-formula> and Silvaco Atlas simulator. The comparative study in this article provides insights into realistic bridge SHM sensor networks, leveraging solar energy harvesting and switched-beam antennas.</b>

Modeling of an aircraft structural health monitoring sensor network for operational impact assessment

Structural health monitoring (SHM) of aircraft components can improve maintenance operations, potentially reducing costs for inspections, unscheduled maintenance events, and unexpected delays. On the other hand, aircraft safety and net present value can be adversely influenced by false alarms, missed detections, system costs, and weight and power requirements of the SHM system. In order to gain a better understanding into the latter, we present a weight and power model for a sensor network, comprising sensors, interrogators, data collectors, and wiring. We assess the net benefit of using SHM in terms of reduced expenditure as function of network coverage, considering a corresponding potential in reducing the inspection effort.

Design of structural monitoring sensor network using surrogate modeling of stochastic sensor signal

Abstract Efficient Structural Health Monitoring (SHM) could reduce operation and maintenance costs, improve longevity, and enhance safety of the performance in complex mechanical systems. Traditional sensor network for SHM relied on simple sensor behavior without considering uncertain factors from system and environment. A probabilistic sensing model is required to simulate the realistic and stochastic sensor performance in the sensor network design process. In this paper, we introduce reliability-based design optimization for piezoelectric sensor network considering the detectability of different failure modes. The proposed method diagnoses failure based on the Mahalanobis Distance (MD) suitable for many SHM processes, while considering the uncertainties from structure properties and operation condition. Kriging is applied for surrogate modeling of the stochastic sensor signal and reduce computational cost. The optimal piezoelectric sensor network design is prototyped and its failure detection capability is experimentally verified.

Cost⁻Benefit Optimization of Structural Health Monitoring Sensor Networks.

Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design

Structural health monitoring (SHM) may be exploited to estimate the mechanical properties of existing structures and check for potential damage. Among commonly used methodologies for property characterization, the Bayesian approach holds the lead because it is endowed with the particular advantage of quantifying associated uncertainties. These uncertainties arise owing to diverse factors including (1) sensor accuracy and positioning, (2) environmental influences, and (3) modeling errors. In minimizing the influence of sensor-related uncertainties, an optimal design may be adopted for the SHM campaign to maximize the information content of the measurements. Here, a procedure based on Bayesian experimental design is proposed to quantify the expected utility of the sensor network. The positions of the used sensors are selected in a way that maximizes the Shannon information gain between the prior and posterior probability distributions of the parameters to be estimated. In order to numerically solve the resulting optimization problem, surrogate models based on polynomial chaos expansion (PCE) and stochastic optimization methods are used. The use of surrogates allows one to reduce the computational cost of the associated model runs. The method is applied to a large-scale example, namely the Pirelli Tower in Milan.

Virtual Environments for Visualizing Structural Health Monitoring Sensor Networks, Data, and Metadata.

Visualization of sensor networks, data, and metadata is becoming one of the most pivotal aspects of the structural health monitoring (SHM) process. Without the ability to communicate efficiently and effectively between disparate groups working on a project, an SHM system can be underused, misunderstood, or even abandoned. For this reason, this work seeks to evaluate visualization techniques in the field, identify flaws in current practices, and devise a new method for visualizing and accessing SHM data and metadata in 3D. More precisely, the work presented here reflects a method and digital workflow for integrating SHM sensor networks, data, and metadata into a virtual reality environment by combining spherical imaging and informational modeling. Both intuitive and interactive, this method fosters communication on a project enabling diverse practitioners of SHM to efficiently consult and use the sensor networks, data, and metadata. The method is presented through its implementation on a case study, Streicker Bridge at Princeton University campus. To illustrate the efficiency of the new method, the time and data file size were compared to other potential methods used for visualizing and accessing SHM sensor networks, data, and metadata in 3D. Additionally, feedback from civil engineering students familiar with SHM is used for validation. Recommendations on how different groups working together on an SHM project can create SHM virtual environment and convey data to proper audiences, are also included.

Virtual sensors for SHM using isogeometric piezoelectric finite elements

Purpose– A design of sensor networks for structural health monitoring (SHM) with guided waves poses a hard challenge. Therefore different approaches are possible. A known one is the usage of probability of detection (POD) criteria. Here, areas of potential impact sensitivity are calculated for every sensor which leads to a POD. The number of sensors is increased until a demanded POD is reached. However, these calculations are usually based on finite element methods and underlie different assumptions and approximations which can cause different inaccuracies. These limitations are avoided by using an experimental data basis for virtual sensors in this paper. The paper aims to discuss these issues.Design/methodology/approach– An air-coupled ultrasound scanning technique is used for guided wave investigations. Recorded displacements of a structure surface are used as stimulation of virtual sensors which can be designed by software and positioned within available data field. For the calculation of sensor signals an isogeometric finite element model is used. The virtually bonded layer of the virtual piezoceramic sensor interpolates with non-uniform rational B-Splines (NURBS) the measured nodal data for each time step. This interpolation corresponds to a displacement boundary condition and is used to calculate the electrical potential at the free surface of the sensor.Findings– Experimental data based on air-coupled ultrasound scanning technique can be used for elimination of disadvantages in numerical simulations by developing sensor networks for SHM. In combination with a transfer matrix method (TM) a three-dimensional displacement of specimen surface for complex composites can be calculated. To obtain the sensor signal a surface-bonded sensor is modeled by an isogeometric finite element approach. A good accordance is found between calculated virtual sensor signal and its experimental verification.Research limitations/implications– Some deviations between calculated signal and its experimental verification are mainly justified by different spectral transfer functions between wave field scanning technique and signal recording of applied sensors. Furthermore, sensor influence on wave propagation is neglected in the presented method.Originality/value– In this paper, the principle of virtual sensors is applied on anisotropic multilayered lamina by using isogeometric finite elements for piezoelectric sensors. This enables any sensor dimension, layout and position on complex composites. Furthermore a bonding layer between specimen and sensor is considered. The method allows a detailed analysis of sensor behavior on a specimen surface and the design and optimization of entire sensor networks for SHM.

Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human–machine cooperative interface

For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human–machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human–machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human–machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

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.

BILBO Network: a proposal for communications in aircraft Structural Health Monitoring sensor networks

In the aeronautical environment, numerous regulatory and communication protocols exist that cover interconnection of on-board equipment inside the aircraft. Developed and implemented by the airlines since the 1960s, these communication systems are reliable, strong, certified and able to contact different sensors distributed throughout the aircraft. However, the scenario is slightly different in the structural health monitoring (SHM) field as the requirements and specifications that a global SHM communication system must fulfill are distinct. The number of SHM sensors installed in the aircraft rises into the thousands, and it is impossible to maintain all of the SHM sensors in operation simultaneously because the overall power consumption would be of thousands of Watts. This design of a new communication system must consider aspects as management of the electrical power supply, topology of the network for thousands of nodes, sampling frequency for SHM analysis, data rates, selected real-time considerations, and total cable weight. The goal of the research presented in this paper is to describe and present a possible integration scheme for the large number of SHM sensors installed on-board an aircraft with low power consumption. This paper presents a new communications system for SHM sensors known as the Bi-Instruction Link Bi-Operator (BILBO).

A bio-inspired asynchronous skin system for crack detection applications

In many applications of structural health monitoring (SHM) it is imperative or advantageous to have large sensor arrays in order to properly sense the state of health of the structure. Typically these sensor networks are implemented by placing a large number of sensors over a structure and running individual cables from each sensor back to a central measurement station. Data is then collected from each sensor on the network at a constant sampling rate regardless of the current timescales at which events are acting on the structure. These conventional SHM sensor networks have a number of shortfalls. They tend to have a large number of cables that can represent a single point of failure for each sensor as well as add significant weight and installation costs. The constant sampling rate associated with each sensor very quickly leads to large amounts of data that must be analyzed, stored, and possibly transmitted to a remote user. This leads to increased demands on power consumption, bandwidth, and size. It also taxes our current techniques for managing large amounts of data. For the last decade the goal of the SHM community has been to endow structures with the functionality of a biological nervous system. Despite this goal the community has predominantly ignored the biological nervous system as inspiration for building structural nervous systems, choosing instead to focus on experimental mechanics and simulation techniques. In this work we explore the use of a novel, bio-inspired, SHM skin. This skin makes use of distributed computing and asynchronous communication techniques to alleviate the scale of the data management challenge as well as reduce power. The system also periodically sends a ‘heat beat’ signal to provide state-of-health updates. This conductive skin was implemented using conductive ink resistors as well as with graphene-oxide capacitors.

An UHF-Band wireless power transfer system for structural health monitoring sensor network

An UHF-Band wireless power transfer system for structural health monitoring sensor network

A web-based structural health monitoring sensor network

This paper presents a web-based structural health monitoring (SHM) sensor network for sensor data visualisation, data processing and damage diagnosis. Most existing web-based monitoring systems employ a centralised web server. The severe limitation of communication bandwidth in wireless sensor networks, however, limits the application of the centralised web server approach, especially in SHM networks which have high sampling rate. To reduce the amount of sensor data to be transmitted, the presented web-based SHM sensor network provides web services at sensor node level for real-time sensor data visualisation, data processing and damage diagnosis. Web services are available both on sensor nodes and a data repository machine with a web server. The ability of directly accessing data on sensor nodes via internet allows users to monitor the performance of a structure in a timely manner. The presented web-based system has been tested using a scaled steel bridge structure. The test results show the feasibility of using this web-based system for the real-time sensor data visualisation and damage diagnosis.

Mobile Agent Computing Paradigm for Building a Flexible Structural Health Monitoring Sensor Network

Wireless structural health monitoring research has drawn great attention in recent years from various research groups. While sensor network approach is a feasible solution for structural health monitoring, the design of wireless sensor networks presents a number of challenges, such as adaptability and the limited communication bandwidth. To address these challenges, this paper explores the mobile agent approach to enhance the flexibility and reduce raw data transmission in wireless structural health monitoring sensor networks. An integrated wireless sensor network consisting of a mobile agent-based network middleware and distributed high computational power sensor nodes is developed. These embedded computer-based high computational power sensor nodes include Linux operating system, integrated with open source numerical libraries, and connected to multimodality sensors to support both active and passive sensing. The mobile agent middleware is built on a mobile agent system called Mobile-C. The mobile agent middleware allows a sensor network moving computational programs to the data source. With mobile agent middleware, a sensor network is able to adopt newly developed diagnosis algorithms and make adjustment in response to operational or task changes. The presented mobile agent approach has been validated for structural damage diagnosis using a scaled steel bridge.

A mobile sensing system for structural health monitoring: design and validation

This paper describes a new approach using mobile sensor networks for structural healthmonitoring. Compared with static sensors, mobile sensor networks offer flexible systemarchitectures with adaptive spatial resolutions. The paper first describes the design of amobile sensing node that is capable of maneuvering on structures built with ferromagneticmaterials. The mobile sensing node can also attach/detach an accelerometer onto/from thestructural surface. The performance of the prototype mobile sensor network has beenvalidated through laboratory experiments. Two mobile sensing nodes are adopted fornavigating on a steel portal frame and providing dense acceleration measurements.Transmissibility function analysis is conducted to identify structural damage using datacollected by the mobile sensing nodes. This preliminary work is expected to spawntransformative changes in the use of mobile sensors for future structural health monitoring.

A Mobile Host Approach for Wireless Powering and Interrogation of Structural Health Monitoring Sensor Networks

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can provide the data collection necessary for rapid structural assessment after an event such as a natural disaster puts the reliability of civil infrastructure in question. Technical challenges affecting deployment of such a network include ensuring power is maintained at the sensor nodes, reducing installation and maintenance costs, and automating the collection and analysis of data provided by a wireless sensor network. In this work, a new WSN paradigm is presented. This architecture utilizes nodes that are deployed without resident power. The associated sensors operate on a mechanical memory principle. A mobile host, such as a robot or unmanned aerial vehicle, is used on an as-needed basis to charge the node by wireless power delivery and subsequently retrieve the data by wireless interrogation. The mobile host may be guided in turn to any deployed node that requires interrogation. The contribution of this work is the first field demonstration of a mobile host wireless sensor network. The sensor node, referred to as THINNER, capable of collecting data wirelessly in the absence of electrical power was developed. A peak displacement sensor capable of interfacing with the THINNER sensor node was also designed and tested. A wireless energy delivery package capable of being carried by an airborne mobile host was developed. Finally, the system engineering required to implement the overall sensor network was carried out. The field demonstration took place on an out-of-service, full-scale bridge near Truth-or-Consequences, NM.

Energy Harvesting for Structural Health Monitoring Sensor Networks

This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks, and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that many of the technologies discussed herein are applicable to powering any type of low-power embedded sensing system regardless of the application.