Structural Analysis and Damage Detection in Composite Materials Applications

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.

This paper details some significant findings on the use of the fiber Bragg grating (FBG) sensors for structural health monitoring (SHM) in aerospace fiber reinforced polymer (FRP) structures. A diminutive sensor provides a capability of imbedding inside FRP structures to monitor vital locations of damage. Some practical problems associated with the implementation of FBG based SHM systems in the aerospace FRP structures such as the difficulty of embedding FBG sensors during the manufacturing process and interrelation of distortion to FBG spectra due to internal damage, and other independent effects will be thoroughly studied. An innovative method to interpret FBG signals for identifying damage inside the structures will also be discussed.

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.

Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network

As civil infrastructure (i.e. bridges, railways, and tunnels) continues to age; the frequency and need to perform inspection more quickly on a broader scale increases. Traditional inspection and monitoring techniques (e.g., visual inspection, mechanical sounding, rebound hammer, cover meter, electrical potential measurements, ultrasound, and ground penetrating radar) may produce inconsistent results, require lane closure, are labor intensive and time-consuming. Therefore, new structural health monitoring systems must be developed that are automated, highly accurate, minimally invasive, and cost effective. Three-dimensional (3D) digital image correlation (DIC) systems have the merits of extracting full-field strain, deformation, and geometry profiles. These profiles can then be stitched together to generate a complete integrity map of the area of interest. Concurrently, unmanned aerial vehicles (UAVs) have emerged as valuable resources for positioning sensing equipment where it is either difficult to measure or poses a risk to human safety. UAVs have the capability to expedite the optical-based measurement process, offer increased accessibility, and reduce interference with local traffic. Within this work, an autonomous unmanned aerial vehicle in conjunction with 3D DIC was developed for monitoring bridges. The capabilities of the proposed system are demonstrated in both laboratory measurements and data collected from bridges currently in service. Potential measurement influences from platform instability, rotor vibration and positioning inaccuracy are also studied in a controlled environment. The results of these experiments show that the combination of autonomous flight with 3D DIC and other non-contact measurement systems provides a valuable and effective civil inspection platform.

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

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.

Additive manufacturing (AM) is a name for techniques applied to constructing threedimensional (3D) objects in a layer-by-layer process. AM methods can be applied for creating elements from fiber (glass/ carbon) reinforced polymers. During the process, fiber Bragg grating (FBG) sensors can be embedded into the element structure for the purpose of structural health monitoring (SHM) system development. Such an approach combines in one, advantages of AM (limited waste, elements with complex shape) and SHM system (safety, information about real loading conditions). Such a method can be applied for the manufacturing of different elements applied in many branches of industry, e.g. marine or civil engineering. The goal of the paper is to analyze the temperature influence on AM fiber (glass/ carbon) reinforced polymer structures with embedded FBG sensors. The analyzes will be focused on the influence of FBG sensors on material durability. Additionally, the influence of elevated temperatures on the finished AM elements will be investigated.

During the last decades, structural health monitoring (SHM) systems are used in order to detect damage in structures. We have developed a novel structural health monitoring approach, the so-called “effective structural health monitoring” (eSHM) system. The current SHM system is incorporated into a metallic structure by means of additive manufacturing (AM) and has the possibility to advance life safety and reduce direct operative costs. It operates based on a network of capillaries that are integrated into an AM structure. The internal pressure of the capillaries is continuously monitored by a pressure sensor. When a crack nucleates and reaches the capillary, the internal pressure changes signifying the existence of the flaw. The main objective of this paper is to evaluate the crack detection capacity of the eSHM system and crack location accuracy by means of various non-destructive testing (NDT) techniques. During this study, detailed acoustic emission (AE) analysis was applied in AM materials for the first time in order to investigate if phenomena like the Kaiser effect and waveform parameters used in conventional metals can offer valuable insight into the damage accumulation of the AM structure as well. Liquid penetrant inspection, eddy current and radiography were also used in order to confirm the fatigue damage and indicate the damage location on un-notched four-point bending AM metallic specimens with an integrated eSHM system. It is shown that the eSHM system in combination with NDT can provide correct information on the damage condition of additive manufactured metals.

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.

Floating covers used in waste water treatment plants are one of the many structures formed with membrane materials. These structures are usually large and can spread over an area measuring 470 m × 170 m. The aim of this paper is to describe recent work to develop an innovative and effective approach for structural health monitoring (SHM) of such large membrane-like infrastructure. This paper will propose a potentially cost-effective non-contact approach for full-field strain and stress mapping using an unmanned aerial vehicle (UAV) mounted with a digital camera and a global positioning system (GPS) tracker. The aim is to use the images acquired by the UAV to define the geometry of the floating cover using photogrammetry. In this manner, any changes in the geometry of the floating cover due to forces acting beneath resulting from its deployment and usage can be determined. The time-scale for these changes is in terms of weeks and months. The change in the geometry can be implemented as input conditions to a finite element model (FEM) for stress prediction. This will facilitate the determination of the state of distress of the floating cover. This paper investigates the possibility of using data recorded from a UAV to predict the strain level and assess the health of such structures. An investigation was first conducted on a laboratory sized membrane structure instrumented with strain gauges for comparison against strains, which were computed from 3D scans of the membrane geometry. Upon validating the technique in the laboratory, it was applied to a more realistic scenario: an outdoor test membrane structure and capable UAV were constructed to see if the shape of the membrane could be computed. The membrane displacements were then used to calculate the membrane stress and strain, state demonstrating a new way to perform structural health monitoring on membrane structures.

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.

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.