Integration Of Structural Health Monitoring Research Articles

Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: Structural Health Monitoring in the Context of Industry 4.0

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Bird Strike Test Event Diagnosis Using a Hybrid SHM System

Several advances have been made in the recent years in the integration of Structural Health Monitoring (SHM) systems within Structural Integrity (SI) management. The current state-of-the-art Airbus Defence and Space (ADS) SHM System relies on both an analytical elastic wave propagation model and a Dynamic Distributed Fibre Optics Sensing (DFOS) Environmental and Operational Conditions (EOC) measurement and compensation model. This hybrid SHM system is able to properly diagnose mechanical impacts (events) and resulting damages that may occur during aircraft operation, while accounting for punctual alterations in the structure due to changes in ambient temperature or pressure. In this paper, a demonstrative application of this system is presented for the diagnosis of a Bird Strike Test Event. The test has been carried out in controlled conditions for a hybrid metal-composite fuselage cockpit structure, within the scope of CS2 PASSARO EU program. The results show the reliability of the ADS SHM system for supervised conditions.

Design of self-healing materials for the integration of structural health monitoring (SHM^2)

Integration of the complementary fields of structural health monitoring in self-healing materials (SHM2) has the potential to transform the traditional engineering concepts of fail safe and damaged tolerant design. Structural health monitoring seeks to embed and automate sensing capabilities within structures to determine their state and to detect degradation or damage. Once degradation or damage is detected, additional decisions and potential action are required. Reducing operational envelopes can prevent further damage accumulation or catastrophic failure or repairs can be performed to correct the degradation. Typical repair requires active involvement of personnel and potentially down time. Self-healing materials seek to avoid maintenance needs and down time through the creation of structures and materials with an imbued capability to repair damage. Together SHM^ 2 has the potential to create a closed loop where damage is detected using embedded sensors and automated analysis that can then initiate self-healing. The highly structured and controlled nature of self-healing materials presents an opportunity to design structures that additionally support health monitoring capabilities. This paper presents recent research and results informing the design of shape memory alloy (SMA) fiber reinforced metal matrix composites (MMCs) to optimize self-healing capabilities and structural health monitoring (SHM^2). Fiber pull tests were performed to analyze the strength of the interface between the SMA fibers and matrix allowing for sizing to complement both composite design and healing capabilities. Analysis was performed to understand complex failure mechanisms occurring at the interface between detwinning shape memory alloys and the metal matrix. And novel composite design was performed to support both the self-healing and damage detection capabilities of the material structure.

An Improved Monitoring System via the Integration of Structural Health Monitoring and Earthquake Emergency Alert Technologies

As a result of the advantages it offers in construction management and maintenance, structural health monitoring for civil structures is becoming more popular in Europe and throughout the world. The main advantages of using such methods include cheaper inspections, more accurate research into the behavior of structures under dynamic loads, better seismic protection, and real-time or near-real-time observation of structural response and evolution of damage, which helps with post-earthquake scenario production and rescue efforts. As a result, Structural Monitoring Systems is a very multidisciplinary field, in which researchers from a wide variety of fields and institutions may work together to improve the effectiveness and reliability of these systems, the benefits of which are almost self-evident. Nowadays, developments in wireless sensor networks, techniques for determining modal properties, and damage detection systems are among the most urgent issues in the sector. Although modal identification and damage detection algorithms have improved, sensor technology has advanced at a faster rate.

Comparison of unsupervised shallow and deep models for structural health monitoring

Recent failures of bridges worldwide highlight the need for robust structural health surveillance systems to provide alerts for early damage and prevent severe loss. Structural health monitoring (SHM) continues to evolve and gain wide adoption among asset managers to mitigate catastrophic failure risks of structural systems. With the prolific integration of SHM with artificial intelligence and internet of things systems becoming prevalent, several data-driven approaches have gained popularity in SHM. However, it is challenging to obtain ground-truth-labelled damage samples for supervised machine learning methods. A class of shallow machine learning methods known as unsupervised methods (e.g. the one-class support vector machine (OCSVM)), which only uses unlabelled data from a healthy state, has been shown to achieve promising results. However, shallow machine learning methods require laborious expert-crafted feature inputs for optimal performance. Inspired by the automatic feature-extraction ability of deep neural networks, unsupervised methods (deep learning models trained without supervisory ground truth labels) such as autoencoder (AE)-based methods were explored to detect structural damage. Results obtained on both real-world data from Sydney Harbour Bridge, one of Australia's iconic structures, and data collected from laboratory specimens demonstrate the effectiveness of the AE-based method over conventional shallow methods such as the OCSVM.

Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring

Real-time health monitoring of civil infrastructures is performed to maintain their structural integrity, sustainability, and serviceability for a longer time. With smart electronics and packaging technology, large amounts of complex monitoring data are generated, requiring sophisticated artificial intelligence (AI) techniques for their processing. With the advancement of technology, more complex AI models have been applied, from simple models to sophisticated deep learning (DL) models, for structural health monitoring (SHM). In this article, a comprehensive review is performed, primarily on the applications of AI models for SHM to maintain the sustainability of diverse civil infrastructures. Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms easier. UAV is found to be the most promising smart data acquisition technology, whereas convolution neural networks are the most impressive DL model reported for SHM. Furthermore, current challenges and future perspectives of AI-based SHM systems are also described separately. Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems. The integration of SHM with IoT and cloud-based computing is leading us towards the evolution of future smart cities.

Integration of scheduled structural health monitoring with airline maintenance program based on risk analysis

The aerospace industry is striving to reduce the aircraft operating costs while maintaining required safety level. Emerging technologies such as the structural health monitoring to reduce long-term maintenance cost and increase aircraft availability are promoted by the manufacturers. To successfully integrate the structural health monitoring technology into the current maintenance process of modern commercial aviation, a clear definition of the structural-health-monitoring-based maintenance operational concept and the system level requirements is required. This article proposed a structural health monitoring operational concept and the associated maintenance cost modeling and risk assessment methods for the implementation of the structural health monitoring in commercial aviation industry. The developed methodology provides a tool to determine the optimal scheduled structural health monitoring inspection interval and repair decision thresholds for approved scheduled structural health monitoring task. A simulated case study is carried out to demonstrate the structural health monitoring operational concept and how an optimal maintenance strategy can be determined using the proposed methodology. Preliminary results show that the integration of the structural health monitoring into the existing maintenance process can reduce the maintenance cost compared to that of the current practice using the traditional Non-Destructive Evaluation (NDE) techniques while maintaining the risk below an acceptable level.

Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction

Recent catastrophic bridge failures clearly indicate the urgent need for improving interval-based bridge inspection procedures that are qualitative and subjective in nature. Structural Health Monitoring (SHM) can mitigate the deficiencies of interval-based inspection techniques and provide real-time diagnostic information regarding the bridge structural health. SHM is not flawless however; the variability in the vehicle characteristics and traffic operational conditions makes it prone to false diagnosis. Recent advancements in the integration of SHM with intelligent transportation systems (ITS) demonstrate the successful use of ITS devices (e.g., traffic cameras, traffic detectors) in the analysis of bridge responses to multimodal traffic with varying loads or during the critical events that cause excess vibration beyond the normal limit. In an ITS-informed SHM system, the ITS device collected data can be integrated with SHM to increase the reliability and accuracy of the SHM system. This integration would reduce the possibility of false diagnosis of damages detected by the SHM system (e.g., vibrations caused by heavy vehicles on a bridge could be read by a SHM sensor as a structural health problem of the bridge), which would eventually decrease the bridge maintenance costs. Similarly, in SHM-informed ITS system, SHM sensors can provide data on bridge health condition for ITS applications, where ITS uses this bridge health condition information for real-time traffic management. In this paper, literature related to both ITS-informed SHM and SHM-informed ITS is reviewed. Based on the literature review, potential challenges and future research directions associated with ITS-SHM integration are also discussed.

Integration of structural health monitoring with scheduled maintenance of aircraft composite structures

The current aircraft maintenance philosophy, referred to as scheduled maintenance, follows a preventative strategy in which maintenance tasks are carried out at predetermined intervals or according to prescribed criteria related to safety, operational and economic aspects. Emerging technologies, such as structural health monitoring (SHM), are proposed to reduce long-term maintenance cost and increase aircraft availability. It introduces a timely and automatic diagnostic and prognostic capability which brings substantial changes to the current maintenance philosophy. This study investigates an integrated approach of scheduled maintenance and structural health monitoring by suggesting various logical maintenance procedures. A probabilistic model was established to examine the effects of the structural health monitoring synchronisation with scheduled maintenance and the impact on both safety and economy.

Impact location determination on thin laminated composite plates using an NIR-FBG sensor system

An integration of Structural Health Monitoring (SHM) into composite structures contribute towards the development of smart composites structure. Smart-structure offers an ability have a continuous and real-time information of its circumstances under critical loading applications. One of the main objectives in this research study was the development of the Fiber Bragg grating (FBG) sensor system for SHM of thin composite laminates. Not only that, this work has been utilizing the FBG sensors in the Near Infra-red (NIR) range; ∼830nm, as an alternative to the applications of the conventional 1550nm FBG sensors. The capability of this sensor system was validated with the impact location determination test on a thin composite plate. It showed a very promising result whereby the relative error falls below 10%.

Impact source identification in a carbon fiber reinforced polymer plate by using embedded fiber optic acoustic emission sensors

This paper demonstrates the use of four embedded fiber optic acoustic emission sensors (FOAESs) to identify the impact source of the carbon fiber reinforced polymer (CFRP) plate.The optical waveguide method and computer simulation were both applied to explain the principle of FOAES, which proved that any one of the two output terminals of FOAES was able to reveal frequency response characteristics of stress waves. The response characteristics of the FOAES to the stress waves were calibrated and discussed for comparisons of before-and-after embedded in the CFRP plate. Propagation velocities of stress wave depending on the direction in the CFRP plate were measured by FOAESs as the information for impact source identification. A simple and efficient algorithm based on the intersection of hyperbolic curves was presented to estimate the impact source location. An automatic monitoring system with the tailored program of localization algorithm was introduced for online real-time impact source identification. Experiments of impact source localization were achieved by using the FOAESs array mounted on the surface and embedded in the CFRP plate. The measured results agree well with the actual impact sources in the CFRP plate embedded with FOAESs. Furthermore, the research of composites structures embedded with FOAESs is conductive to developing the intelligent integration of structural health monitoring (SHM).

A computational framework for life-cycle management of wind turbines incorporating structural health monitoring

The integration of structural health monitoring into life-cycle management strategies can help facilitating a reliable operation of wind turbines and reducing the life-cycle costs significantly. This article presents a life-cycle management framework for online monitoring and performance assessment of wind turbines, enabling optimum maintenance and inspection planning at minimum associated life-cycle costs. Incorporating continuously updated monitoring data (i.e. structural, environmental, and operational data), the framework allows capturing and understanding the actual wind turbine condition and, hence, reduces uncertainty in structural responses as well as load effects acting on the structure. As will be shown in this article, the framework integrates a variety of heterogeneous hardware and software components, including sensors and data acquisition units, server systems, Internet-enabled user interfaces as well as finite element models for system identification, and a multiagent system for self-detecting sensor malfunctions. To validate its capabilities and to demonstrate its practicability, the framework is deployed for continuous monitoring and life-cycle management of a 500-kW wind turbine. Remote life-cycle analyses of the monitored wind turbine are conducted, and case studies are presented investigating both the structural performance and the operational efficiency of the wind turbine.

Integration of structural health monitoring and fatigue damage prognosis

This paper presents a Bayesian probabilistic methodology to integrate model-based fatigue damage prognosis (FDP) with online and offline structural health monitoring (SHM) data. The prognosis uses fracture mechanics-based fatigue crack growth modeling, along with quantification of various sources of uncertainty, including natural variability, data uncertainty and model errors. These uncertainty sources are connected using a Bayesian network and a probabilistic sensitivity analysis is performed to assess the uncertainty contributions from these sources. The cycle-by-cycle simulation of fatigue crack growth is expedited via the use of a surrogate modeling technique (Gaussian process model) to replace computationally expensive finite element analysis. Real-time monitoring data of external variable amplitude loading history is used to construct a Bayesian autoregressive integrated moving average (ARIMA) model to predict and update the loading. On-ground crack inspection data is used to quantify the uncertainty in the initial and current size of an existing crack, using the Bayesian approach. Three possible cases of inspection results are considered: (1) crack is not detected; (2) crack is detected but not measured; (3) crack is detected and measured. Different scenarios of data availability (load monitoring data and inspection data) are considered for the prognosis of an individual component in a fleet. A numerical example, surface cracking in a rotorcraft mast under service loading, is implemented to illustrate the proposed methodology. The results of prognosis are validated using Bayesian hypothesis testing.

Design of copper/carbon-coated fiber Bragg grating acoustic sensor net for integrated health monitoring of nuclear power plant

A nuclear power plant (NPP) is a harsh environment that gives rise to age-related degradation of the plant structures, and eventually leads to radiation leakage that threatens humans. Integrated structural health monitoring (ISHM) technology is a strong candidate for the prevention of the NPP accidents during operation. Prior studies have shown that fiber Bragg gratings (FBGs) and metal-coated fibers have good radiation and high temperature resistance. In this study, a FBG acoustic sensor using a metallic adhesive for installation and a relatively economical copper/carbon (Cu/C)-coated fiber is developed for ISHM of high temperature NPP structures. A chemical method is proposed to remove the Cu/C coating. A 5 mm FBG was successfully inscribed in a Ge-doped silica core through a 7 mm-long silica section with the coating removed. The Cu/C-coated fiber with the same core/clad structure as the standard SMF allowed no-loss fusion splicing, and showed good adaptability to the economical standard fiber, adaptor, connector, and instruments. It showed also good thermal resistance (<345 °C) with no degradation in optical power during the optical transmission. The metallic adhesive used to install the FBG in a one-end-free configuration showed superior bonding reliability during temperature cycles ranging from 25 °C to 345 °C. The FBG reflectivity was stabilized at a 58% drop from the initial reflectivity, and the Cu/C-coated FBG sensor using the metallic adhesive could successfully detect the acousto-ultrasonic waves generated by pencil lead breaking and laser beam excitation.

Design of Fiber Bragg Grating Acoustic Sensor for Structural Health Monitoring of Nuclear Power Plant

Nuclear power plant (NPP) is commonly categorized as a harsh environment that gives rise to age-related degradation on the structures of plant and eventually leads to radiation leakage that threatens human. Integrated structural health monitoring (ISHM) technology is a strong candidate for NPP accidents. The emergence of optical fiber technology into SHM system for NPP was greatly interested by researchers, and also prior research works have shown that the fiber Bragg grating (FBG) was able to retain its reflectivity under radiation exposure. In this paper, a metal-coated fiber was newly used to develop a FBG acoustic sensor for ISHM of NPP. The 7 mm length of aluminum, copper/carbon coatings were successfully removed with sodium hydroxide and nitric acid solutions. A 5 mm FBG was successfully inscribed in the silica core through the 7 mm long coating-removed silica section of copper/carbon-coated fiber and the initial reflectivity was 71%. Then, the FBG acoustic sensor was developed in one-end-free FBG configuration on the stainless steel vessel using a high temperature metallic adhesive. The reflective power of the sensor was stabilized at 345°C during the high temperature elevation cyclic process. The FBG acoustic sensor showed good response to the acousto-ultrasonic waves during pencil-lead breaking and laser ultrasonics tests. The high temperature FBG acoustic sensor written in the cost-effective metal/carbon is feasible to be used for ISHM of the NPP.

Integration of structural health monitoring in life-cycle performance assessment of ship structures under uncertainty

In this paper, an approach for integrating the data obtained from structural health monitoring (SHM) in the life-cycle performance assessment of ship structures under uncertainty is presented. Life-cycle performance of the ship structure is quantified in terms of the reliability with respect to first and ultimate failures and the system redundancy. The performance assessment of the structure is enhanced by incorporating prior design code-based knowledge and information obtained by SHM using Bayesian updating concepts. Advanced modeling techniques are used for the hull strength computations needed for the life-cycle performance analysis. SHM data obtained by testing a scaled model of a Joint High-speed Sealift Ship is used to update its life-cycle performance.

Integration of structural health monitoring in a system performance based life-cycle bridge management framework

In this paper, an approach for integrating the information obtained from structural health monitoring in a life-cycle bridge management framework is proposed. The framework is developed on the basis of life-cycle system performance concepts that are also presented in this paper. The performance of the bridge is quantified by incorporating prior knowledge and information obtained from structural health monitoring using Bayesian updating concepts. This performance is predicted in the future using extreme value statistics. Advanced modelling tools and techniques are used for the lifetime reliability computations, including incremental nonlinear finite element analyses, quadratic response surface modelling using design of experiments concepts, and Latin hypercube sampling, among other techniques. The methodology is illustrated on an existing bridge in the state of Wisconsin.