Damage discrimination on drone fuselage with OBR Based Fibre-Optic Networks

Primary and secondary aircraft structures are commonly made from laminated composite materials. Unfortunately, the interlaminar tensile and shear strengths are many orders of magnitude less than the in-plane strengths because they are driven by the strength of the matrix as opposed to that of the fibers. Moreover, the matrix strength is brittle in nature so that it is difficult to monitor an impending overload or fatigue failure. In order to avoid failures due to interlaminar damage without using high margins of safety, a Structural Health Monitoring (SHM) system can be used. While extensive literature on SHM systems for fiber-driven damage exists, there is no mention of an SHM system that specifically addresses these types of matrix-driven damage. The proposed SHM system spans the acquisition, location, reconstruction, and damage type decision of Acoustic Emission (AE) damage signals. An algorithm is presented to calculate the location of AE damage signals in a laminated composite structure using an over-determined approach to minimize location errors. Also, a novel procedure is presented to simultaneously reconstruct the loading source time series for a recorded damage signal and identify the source type. An example analytical study is presented to calculate the effects of uncertainty on the location and damage type predictions. Two experimental studies were undertaken to exercise the SHM algorithm presented in this study. The first experiment studies a mixed damage blunt impact case on a flat composite plate for different impact energies. The second experiment studies blunt impact of a reinforced composite plate that models a composite wing skin with bonded stringers and ribs.

Composite aircraft structures commonly use bonded joints to adhere the wing and fuselage skins to the wing spars, stringers, ribs, and frames. Unfortunately the bond out-ofplane normal (peel) strength is many orders of magnitude less than the fiber strength of the adjacent laminates. Moreover, the adhesive failure is brittle in nature so that it is difficult to monitor an impending overload or fatigue failure. In order to avoid failures due to interlaminar damage without using high margins of safety, a Structural Health Monitoring (SHM) system can be used. While extensive literature on SHM systems for fiber-driven damage exists, there is no mention of an SHM system that specifically addresses these types of matrix-driven damage. The proposed SHM system spans the acquisition, location, reconstruction, and damage type decision of Acoustic Emission (AE) damage signals. An algorithm is presented to calculate the location of AE damage signals in a laminated composite structure using an over-determined approach to minimize location errors. Also, a novel procedure is presented to simultaneously reconstruct the loading source time series for a recorded damage signal and identify the source type. Parametric studies are presented for both algorithms to gauge their effectiveness under varying levels of noise, number of sensors, material properties and different types of uncertainty. Two experimental studies were undertaken to exercise the SHM algorithm presented in this study. The first experiment models a predominantly single type quasi-static damage case, and the second experiment studies a mixed damage impact case.

The continuous Non-Destructive Evaluation of assets for long-term assurance of performance has led to several developments over the deployment of a Real-Time Structural Health Monitoring (SHM) system. Considering the challenges involved under the implementation of an SHM system for the applications working under harsh environmental conditions with limited access to power sources this work is aimed to contribute towards overcoming those challenges by using the noise from the structure’s machinery or any ambient source as an alternative energy source and employing Fiber Optics based sensing, for its applicability under harsh environments. The required SHM system is realized with the cross-correlation of a fully diffused noise field, sensed using the Fiber Bragg Grating (FBG) sensors at two random locations. With no control on the input received as noise, to this end, a method is developed based on a Deep Learning framework, which is aimed towards a Universal Deployment of the passive SHM system. The methodology is designed to perform the health monitoring of the system, independent of the input perturbations. The validation performed on simulation data has demonstrated the feasibility of the developed technique towards the required kind of passive SHM system.

Structural health monitoring (SHM) systems collect and analyze data recorded from civil infrastructure to detect changes in the structural condition and to predict potential damage. However, sensor faults in SHM systems may lead to erroneous structural assessments, thus to inefficient maintenance operations, which could jeopardize the integrity of the structures. Artificial intelligence (AI) algorithms based on analytical redundancy have proven being effective foundations to implement sensor fault diagnosis but are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. Moreover, operating with centralized servers increases the likelihood of single points of failure, which compromises the robustness of SHM systems. To overcome the limitations and to enhance the robustness of SHM systems, this paper proposes a decentralized approach towards fault-tolerant SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Fault diagnosis algorithms based on AI are embedded into sensor nodes deployed for SHM, using Internet of Things technologies to be computed at the edge of civil infrastructure, avoiding large amounts of data to be sent to central servers. In addition, the sensor nodes embed algorithms that collect, process, and analyze the sensor data to assess the condition of the structures being monitored. The result is a smart (i.e., decentralized, fault-tolerant, and AIoT-based) SHM system that is calibrated in a laboratory experiment and then deployed on a bridge for field validation. It is expected that the approach presented in this paper will improve the robustness of SHM systems in assessing the structural condition of civil infrastructure.

Structure damage modelling for the simulation of elastic wave propagation is essential for simulation-based testing of structural health monitoring (SHM) systems. Reflection, transmission and other complex phenomena related to elastic wave interaction with structure defects need to be reproduced by the simulation method to replace physical testing by numerical one and aid the design process of SHM system. Authors review commonly used methods for the simulation of elastic wave propagation pointing out their advantages and disadvantages. Subsequently methods for crack and delamination modelling are presented. Proposed models are used for numerical testing of designed SHM system. Simulation results are compared with experimental ones, showing very good agreement. Finally, structure damage models are used for testing delamination localization algorithm.

Fault diagnosis (FD), comprising fault detection, isolation, identification and accommodation, enables structural health monitoring (SHM) systems to operate reliably by allowing timely rectification of sensor faults that may cause data corruption or loss. Although sensor fault identification is scarce in FD of SHM systems, recent FD methods have included fault identification assuming one sensor fault at a time. However, real-world SHM systems may include combined faults that simultaneously affect individual sensors. This paper presents a methodology for identifying combined sensor faults occurring simultaneously in individual sensors. To improve the quality of FD and comprehend the causes leading to sensor faults, the identification of combined sensor faults (ICSF) methodology is based on a formal classification of the types of combined sensor faults. Specifically, the ICSF methodology builds upon long short-term memory (LSTM) networks, i.e. a type of recurrent neural networks, used for classifying ‘sequences’, such as sets of acceleration measurements. The ICSF methodology is validated using real-world acceleration measurements from an SHM system installed on a bridge, demonstrating the capability of the LSTM networks in identifying combined sensor faults, thus improving the quality of FD in SHM systems. Future research aims to decentralize the ICSF methodology and to reformulate the classification models in a mathematical form with an explanation interface, using explainable artificial intelligence, for increased transparency.

Near real-time tracking of dynamic properties for standalone structural health monitoring systems

In this paper, a structural health monitoring (SHM) system based on multi-scale cross-sample entropy (MSCE) is proposed for detecting damage locations in multi-bay three-dimensional structures. The location of damage is evaluated for each bay through MSCE analysis by examining the degree of dissimilarity between the response signals of vertically-adjacent floors. Subsequently, the results are quantified using the damage index (DI). The performance of the proposed SHM system was determined in this study by performing a finite element analysis of a multi-bay seven-story structure. The derived results revealed that the SHM system successfully detected the damaged floors and their respective directions for several cases. The proposed system provides a preliminary assessment of which bay has been more severely affected. Thus, the effectiveness and high potential of the SHM system for locating damage in large and complex structures rapidly and at low cost are demonstrated.

To ensure safety and continuous operation of dynamic systems, including civil structures, mechanical and aerospace systems, structural health monitoring and control systems aim to study and optimize parameters that govern their behavior. The development of any intelligent infrastructure systems must take into account the specific challenges of each system and provide customized solutions to address those challenges effectively. In this dissertation, a structural health monitoring system and a passive control system are explored. The structural health monitoring system is developed to estimate the behavior of a special group of systems experiencing large accelerations, higher than 100 $g$, for durations less than 100 ms called high-rate systems. The high-rate systems are characterized by 1) large uncertainties in their external loads, 2) high levels of nonstationarity and heavy disturbances, and 3) unmodeled dynamics from changes in system configuration. A neural-network structural health monitoring system is proposed and customized to address the challenges of such systems. The passive control system assesses the performance of a novel passive variable friction damper (PVFD) in achieving multi-objective vibration mitigation. A probabilistic performance-based design framework is presented to select the optimum parameters of the system. In order to effectively implement the systems of interest of this study, methods for effective system parameter selection is investigated. The algorithms presented allow for informed parameter selection in a large space of possible values. The methods have been verified on a number of case studies and have shown promise for effective monitoring and control.

Abstract: Bridge vortex induced vibration (VIV) is a resonance phenomenon caused by the periodic shedding of vortices generated by natural wind passing through bridges. Bridge VIVs will not only cause fatigue damage to the structures but also affect driving safety for the passing vehicles. With the popularization of structural health monitoring (SHM) systems, machine learning technology is widely used in the field of vortex induced vibration identification for long-span bridges, due to its intelligence, real-time performance, and sensitivity to data. However, although traditional machine learning algorithms can identify the VIV based on the response data history of long-span bridges collected by SHM systems, they are difficult to apply to bridges which do not have historical vortex induced vibration data. Therefore, this paper proposes an adaptive transfer learning method for identifying VIV in the main girder of long-span suspension bridges. The proposed method can identify VIV without VIV history data of the target bridge. Results show that it can well identify VIVs at the earlier stage based on the SHM datasets of two long-span suspension bridges, verifying its effectiveness and generalization ability.

Validation of the performance of guided wave structural health monitoring systems is vital if they are to be widely deployed; testing the damage detection ability of a system by introducing different types of damage at varying locations is very costly and cannot be performed on a system in operation. Estimating the damage detection ability of a system solely by numerical simulations is not possible as complex environmental effects cannot be accounted for. In this study, a methodology was tested and verified that uses finite element simulations to superimpose defect signals onto measurements collected from a defect-free structure. These signals are acquired from the structure of interest under varying environmental and operational conditions for an initial monitoring period. Measurements collected in a previous blind trial of an L-shaped pipe section, onto which a number of corrosion-like defects were introduced, were utilised during this investigation. The growth of three of these defects was replicated using finite element analysis and the simulated reflections were superimposed onto signals collected on the defect-free test pipe. The signal changes and limits of reliable detection predicted from the synthetic defect reflections superimposed on the measurements from the undamaged complex structure agreed well with the changes due to real damage measured on the same structure. This methodology is of great value for any structural health monitoring system as it allows for the minimum detectable defect size to be estimated for specific geometries and damage locations in a quick and efficient manner without the need for multiple test structures while accounting for environmental variations.

The strategic and monetary value of the civil infrastructure worldwide necessitates the development of structural health monitoring (SHM) systems that can accurately monitor structural response due to real-time loading conditions, detect damage in the structure, and report the location and nature of this damage. In the last decade, extensive research has been carried out for developing vibration-based damage detection algorithms that can relate structural dynamics changes to damage occurrence in a structure. In the mean time, the wavelet transform (WT), a signal processing technique based on a windowing approach of dilated ‘scaled’ and shifted wavelets, is being applied to a broad range of engineering applications. Wavelet transform has proven its ability to overcome many of the limitations of the widely used Fourier transform (FT); hence, it has gained popularity as an efficient means of signal processing in SHM systems. This increasing interest in WT for SHM in diverse applications motivates the authors to write an exposition on the current WT technologies. This article presents a utilitarian view of WT and its technologies. By reviewing the state-of-the-art in WT for SHM, the article discusses specific needs of SHM addressed by WT, classifies WT for damage detection into various fields, and describes features unique to WT that lends itself to SHM. The ultimate intent of this article is to provide the readers with a background on the various aspects of WT that might appeal to their need and sector of interest in SHM. Additionally, the comprehensive literature review that comprises this study will provide the interested reader a focused search to investigate using wavelets in SHM.

Structural Health Monitoring (SHM) systems are widely regarded as capable of significantly reducing inspection costs of safety-critical structures in industries such as aerospace, nuclear, and oil and gas, among others. Successful SHM systems can be considered those which combine good sensitivity to defects, preferably with the capability of localization and identification, with a low sensor density. Techniques based on sparse arrays of sensors which generate and receive guided waves are among the most promising candidates. Guided waves propagate over large distances and certain modes have the ability to transmit through a variety of structural features leading to a relatively small number of distributed sensors being able to cover the structure. In complex structures, which contain high densities of structural elements, the timetraces obtained are often too complex to be directly interpreted due to the large number of overlapping reflections. In this case, the Baseline Subtraction technique becomes attractive. In this method a current signal from the structure is subtracted from a signal which has been acquired during the initial stages of operation of the structure. This eliminates the need for interpretation of the complex raw time signal and any defects will be clearly seen provided the amplitude of the residual signal obtained after subtraction of the baseline signal is sufficiently low when the structure is undamaged. However, it is well known that environmental effects such as stress, ambient temperature variations and liquid loading affect the velocity of guided waves; this modifies the time-traces and leads to high levels of residual signal if a single baseline, taken under different conditions, is used. Of these effects, temperature variations are the most commonly encountered and are critical since they affect not only the wave propagation but also the response of transducers. The present work aims to demonstrate the potential of guided wave health monitoring of large area complex structures. It starts with a general literature review on inspection and monitoring of large area structures, in which the advantages and disadvantages of this technique compared to other well-established SHM techniques 2 are presented. The design and behaviour of two different temperature-stable transducers generating high A0 or S0 mode purity in the sub-200kHz frequency region are described. The efficiency of different signal processing techniques aimed at reducing or eliminating the influence of temperature on wave propagation is evaluated and a temperature compensation signal processing strategy is proposed. Finally, a large metallic structure is used to demonstrate a sparse-array SHM system based on this signal processing strategy, and imaging algorithms are used to combine the information from a large number of sensor combinations, ultimately leading to the localization of defects artificially introduced in the structure.

Recent structural failures such as the I-35W Mississippi River Bridge in Minnesota have underscored the urgent need for improved methods and procedures for evaluating our aging transportation infrastructure. This research seeks to develop a basis for a Structural Health Monitoring (SHM) system to provide quantitative information related to the structural integrity of metallic structures to make appropriate management decisions and ensuring public safety. This research employs advanced structural analysis and nondestructive testing (NDT) methods for an accurate fatigue analysis. Metal railroad bridges in New Mexico will be the focus since many of these structures are over 100 years old and classified as fracture-critical. The term fracture-critical indicates that failure of a single component may result in complete collapse of the structure such as the one experienced by the I-35W Bridge. Failure may originate from sources such as loss of section due to corrosion or cracking caused by fatigue loading. Because standard inspection practice is primarily visual, these types of defects can go undetected due to oversight, lack of access to critical areas, or, in riveted members, hidden defects that are beneath fasteners or connection angles. Another issue is that it is difficult to determine the fatigue damage that a structure has experienced and the rate at which damage is accumulating due to uncertain history and load distribution in supporting members. A SHM system has several advantages that can overcome these limitations. SHM allows critical areas of the structure to be monitored more quantitatively under actual loading. The research needed to apply SHM to metallic structures was performed and a case study was carried out to show the potential of SHM-driven fatigue evaluation to assess the condition of critical transportation infrastructure and to guide inspectors to potential problem areas. This project combines the expertise in transportation infrastructure at New Mexico State University with the expertise at Sandia National Laboratories in the emerging field of SHM.

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.