Parameter optimization for enhanced modeling of dynamic systems

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.

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

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.

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.

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.

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.

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.

Structural health monitoring (SHM) is the process of implementing a damage identification strategy which can be utilized in several applications including aerospace, civil and mechanical engineering infrastructure. Damage is defined as changes to the material and/or geometric properties of these systems. These changes adversely affect the current or future performance of the system. In order to identify damage in a suitable and meaningful manner, the damaged state is compared with other usually undamaged states. This study focuses on a structural health monitoring (SHM) system based on detecting shifts in natural frequencies of the structure. This structural health monitoring system incorporates a low power wireless transmitter that sends a warning signal when damage is detected in a structure. The damage detection technique is implemented on composite structures which are widely used in many applications including aeronautical and aerospace. An automated damage detection system capable of providing information of damage locations based on the finite element analysis and able to compare damage events to other historical data is also proposed in this paper and initially implemented using a microcontroller chip. Moreover, a control methodology using piezoelectric fiber composites, such as active fiber composites (AFCs) and microfiber composites (MFCs), is included as part of the system for vibration suppression purposes. The advantages of using piezoelectric fiber composite actuators are their high performance, flexibility, and durability when compared with the traditional piezoceramic (PZT) actuators. The proposed system may be implemented in many structural components such as aircraft frames and bridges. This SHM technology may help replace the current time-based maintenance scheme with a condition-based one. The condition-based maintenance scheme relies on the ability to monitor the condition of the system and supply information of damage detection to allow a corrective action to be taken.

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.

Objectives: The main objective of this paper to review the various currently available Structural Health Monitoring (SHM) systems for the Polymer Composites particularly in aviation industries. Methods: This paper provides a complete overview about the various types of damage detection technique currently used for the polymer composite structure and also highlights the concept behind the each technique. A brief overview of the research work on experimental and theoretical studies on the various SHM systems is considered and several research papers on these topics are cited. This paper reviews various recent advancement made in the field of damage monitoring techniques, particularly the Piezoresistivity technique with the advent of nanomaterials. Outcomes: This paper serves as an effective source of literature for those interested in pursuing research in the Structural Health Monitoring (SHM) System for the polymer Composites. This paper also provides an effective source of information about the possible research gap in the field the SHM.

Additive manufacturing enables the integration of functionalities during the production process. The effective structural health monitoring (SHM) system relies on the integration of small capillaries inside the structure, of which the pressure is monitored during the operation of the component. The pressure monitoring system allows deriving the loading condition of the part and detects upcoming fatigue failures, thereby creating smart, self-sensing objects. Such technology is of immediate benefit to structurally loaded parts that otherwise require significant inspection and maintenance efforts. Especially those parts, however, have to meet stringent quality standards. The inclusion of such intrusive capillary network, along with the use of a novel manufacturing technique, imposes significant materials and component testing in order to substantiate the added value of such system to metal additively manufactured parts. The current work therefore presents quasi-static materials testing, coupon fatigue testing and component fatigue testing with an embedded SHM system. The (post-) processing conditions of the fabricated parts were varied in order to understand their impact on the mechanical behaviour while attempting to reduce the structural impact of the embedded channels of the effective SHM system.

The Special Issue “Non-Destructive Testing of Structures” has been proposed to present recent developments in the field of diagnostics of structural materials and components in civil and mechanical engineering. The papers highlighted in this editorial concern various aspects of non-invasive diagnostics, including such topics as condition assessments of civil and mechanical structures and connections of structural elements, the inspection of cultural heritage monuments, the testing of structural materials, structural health monitoring systems, the integration of non-destructive testing methods, advanced signal processing for the non-destructive testing of structures (NDT), damage detection and damage imaging, as well as modeling and numerical analyses for supporting structural health monitoring (SHM) systems.

A comprehensive safety evaluation method which can dynamically reflect the damage changes in operation state is established based on the structural health monitoring (SHM) system of a composite girder cable-stayed bridge. The fuzziness and uncertainties during the evaluation process are considered and the random errors are reduced by using the proposed evaluation method. The normal values and safety limits of monitoring indexes of the Guanhe Bridge are proposed by using numerical simulation and monitoring data statistics of the baseline finite element model, and the index evaluation standard of the related code requirements. The results can be used to complement the lack of online monitoring indexes in the existing regulations, and solve the problem that lacking support for quantitative monitoring data in current bridge management system evaluation module, which can provide theoretical basis for the evaluation, management, maintenance and decision-making of bridge structures.

Stochastic characterization of wind field characteristics of an arch bridge instrumented with structural health monitoring system