FLEXIBLE MULTIFUNCTIONAL STRUCTURAL HEALTH MONITORING SYSTEMS FOR INFLATABLE SPACE HABITAT STRUCTURES

The imperative to develop inflatable habitats as a viable alternative to rigid/non-deployable structures is underscored by their demonstrated efficiency in terms of mass and volume, yielding substantial cost savings. NASA's seminal research on inflatable habitats dates back to 1961, exemplified by the space station design initiative undertaken by Goodyear. Although this particular design did not materialize in space, persistent innovation has characterized the trajectory of this industry. Moreover, private sector entities, including Bigelow Aerospace, Sierra Space, Lockheed Martin, and numerous others, have entered the fray, driving significant advancements in inflatable habitat technology. The engagement of these companies signals robust plans for the utilization of inflatable habitats across a spectrum of applications, encompassing commercial, private, and governmental space stations. Within this landscape, our studio has made notable contributions with a concept poised to emerge as a leading innovation in inflatable habitat technology. Our concept exhibits potential for docking with the ISS or serving as an extension to India's ambitious space station plans within the private sector. The paper is delineated into two principal sections: the first section meticulously delineates the development process from the launch payload fairing to the deployment mechanism, leveraging empirical data and technical specifications. The second section offers a comprehensive examination of ancillary services and interior habitat functions, elucidating practical implementations and anticipated outcomes.

Space structures are one of the most critical components for any spacecraft, as they must provide the maximum amount of livable volume with the minimum amount of mass. Deployable structures can be used to gain additional space that would not normally fit under a launch vehicle shroud. This expansion capability allows it to be packed in a small launch volume for launch, and deploy into its fully open volume once in space. Inflatable, deployable structures in particular, have been investigated by NASA since the early 1950’s and used in a number of spaceflight applications. Inflatable satellites, booms, and antennas can be used in low-Earth orbit applications. Inflatable heatshields, decelerators, and airbags can be used for entry, descent and landing applications. Inflatable habitats, airlocks, and space stations can be used for in-space living spaces and surface exploration missions. Inflatable blimps and rovers can be used for advanced missions to other worlds. These applications are just a few of the possible uses for inflatable structures that will continued to be studied as we look to expand our presence throughout the solar system.

This review paper presents new and emerging technologies for structural monitoring of inflatable space structures such as aerospace and space vehicles and habitats. Inflatable structures provide an array of benefits for space operations including low mass and cost, high volume, and versatility. Despite the fact that there has been a fair amount of research on structural monitoring of solid structures in the past, inflatable structures did not receive the attention they deserve until recently. Monitoring an inflatable space vehicle or habitat poses challenges beyond those associated with similar structures on earth. The potential damage caused by micrometeorites and orbital debris (MMOD) requires a rigorous study of the impact to determine the location of impact and, in case of damage, identifying the extent of the damage, and location of the air leak if present. In this review paper, several recent developments in wireless sensing and signal processing that are used for accurate leak detection and localization are presented. Theoretical foundations behind these algorithms and systems, as well as laboratory and field test results (on earth and in space), are presented to paint a complete picture from research and development to flight certification and test.

Complex, high‐rate dynamic structures, such as hypersonic air vehicles, space structures, and weapon systems, require structural health monitoring (SHM) methods that can detect and characterize damage or a change in the system’s configuration on the order of microseconds. While high‐rate SHM methods are an area of current research, there are no benchmark experiments for validating these algorithms. This paper outlines the design of an experimental test bed with user‐selectable parameters that can change rapidly during the system’s response to external forces. The test bed consists of a cantilever beam with electronically detachable added masses and roller constrains that move along the beam. Both controllable system changes can simulate system damage. Experimental results from the test bed are shown in both fixed and changing configurations. A sliding mode observer with a recursive least squares parameter estimator is demonstrated that can track the system’s states and changes in its first natural frequency.

This paper presents an overview of current research and development in the field of sensor placement methods in structural health monitoring. Due to the limitations of equipment facilities and cost, the number of sensors to be installed in a structure is relatively few. In many cases, three are always degrees of freedoms in a structure are not easily accessible, or eventually inaccessible. Moreover, actual structures are all continuous and have essentially infinite degrees of freedoms, therefore, it is impossible to instrument on all the degrees of freedoms. The methods of sensor placement are mainly dealing with accelerometers for global dynamic testing, or structural health monitoring. Three major concerns are discussed, the necessary number of sensors to be installed, where to deploy these sensors, and how to evaluate the effectiveness of these deployed sensors. Finally, a new loading dependent sensor placement method is proposed, and verified by the experimental data of a simple cantilever beam structure.

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

In civil engineering, health monitoring of structure have been treated as an important subject. Health monitoring by using experimental measurements such as strain, acceleration, velocity, displacement, rotation and other parameters has been used for detecting changes, which may indicate damage or degradation. In recent, the applications of health monitoring of large structure have been facilitated by the developments in measurement, communications and computational technology gradually. Especially, large civil structures as bridges are damaged by fatigue loads or unexpected load such as earthquakes and finally reach to failure. Thus, a structural health monitoring and damage detection methods have been developed and attracted a great deal of public attention in recent decades. Actually many research efforts have been conducted to develop a health monitoring and damage detection techniques of existing civil structures. However, these methods are needed to improve to apply to a real civil structure. The purpose of this study is to develop a practical instrument to identify dynamic parameters of real civil structural systems.

Stable Deployment of Rolled-Up Inflatable Tube by Control of Gas Flow

Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

Structural Health Monitoring (SHM) seeks to evaluate the condition of a structure by instrumenting the structure with sensors and then using a combination of structural and data analysis. One unifying factor across all structures is the effect of temperature changes on the behavior of the structure. Temperature Driven - Structural Health Monitoring (TD-SHM) considers temperature as the driving force in structural behavior, and consequently the main input in SHM. Temperature as an input is related to strain and displacement as output to form three-dimensional temperature signatures for the structures. These signatures are descriptive of the thermal behavior, as well as sensitive to changes in structural condition. The two main benefits of TDSHM are the applicability to any structure that undergoes daily or seasonal temperature changes, and the possibility to measure temperature as an input for a complete input-output model. This work examines one type of three-dimensional signature: the relationship between changes in temperature with changes in strain and rotation or tilt on a beam-like concrete structure. The three-dimensional figure is fit with a surface to quantify the behavior, and changes in either the goodness of fit or in the fit itself are indicative of changes in structural behavior. These methods are validated using data from a real structure; Streicker Bridge on campus at Princeton University.

In the context of ageing bridges, buildings, and transportation frameworks, Structural Health Monitoring (SHM) has evolved as an important tool for evaluating the integrity and performance of infrastructure systems. This is particularly true in the context of SHM. It is now possible to do real-time monitoring thanks to the incorporation of sensors that are connected to the Internet of Things (IoT). This capability enables continuous data collecting from important sites of infrastructure. The quality and interpretability of these sensor data, on the other hand, are primarily dependent on the use of robust numerical approaches for signal processing, anomaly detection, and predictive modelling. The purpose of this work is to offer a methodical use of numerical techniques, especially the Finite Element Method (FEM), Euler's Method, and Least Squares Estimation, in the interpretation and utilization of data acquired by Internet of Things-based structural health monitoring (SHM) systems. These mathematical algorithms are shown to be able to identify stress buildup, vibrational anomalies, and deterioration trends in structures by making use of validated datasets derived from well-documented case studies. The technique is verified by means of two rigorous numerical examples that are matched with real-world datasets. These examples demonstrate the effectiveness of the proposed framework in improving the resilience of infrastructure. It has been shown via the findings that the incorporation of numerical computing into SHM that is allowed by the Internet of Things offers a solution for infrastructure management that is scalable, accurate, and proactive. As a result, our study bridges the gap between rigorous mathematical analysis and technical application in the contemporary age of intelligent monitoring.

Assessing the Evolution of Structural Health Monitoring through Smart Sensor Integration

Total Knee Arthroplasty (TKA) continues to be a common and important orthopedic procedure for many in the United States. Despite recent medical advancements and increasing knowledge in the orthopedic community, it has been determined that 20% of TKA patients are still dissatisfied with their knee replacements. Causes of this failure include septic loosening and wear on the bearing component of the implant. Another cause of failure that has received specific attention from the mechanical community is aseptic loosening, which has been attributed to unbalanced ligaments or misalignment of the implant components. Previous efforts have been made to detect loosening by using passive force sensors such as piezoelectric transducers or strain gauges to detect misalignment. An alternative to this is to perform active sensing or structural health monitoring to evaluate possible loosening before it becomes a critical concern to the patient. One method of structural health monitoring, called the electromechanical impedance (EMI) method, is particularly attractive as it can use a single, compact piezoelectric transducer to determine the state of the host structure. This work is intended to evaluate the ability of the EMI method in sensing loosening between the cement and bone of a TKA tibial tray. This work will utilize real tibial trays implanted into synthetic bone (Sawbone) to evaluate the feasibility of detecting loosening using the EMI method. The intention of this work is to serve as a foundation for further in-vivo and intraoperative studies.

A model is generally not needed for the basic damage identification problem when using the electromechanical impedance-based method of structural health monitoring (SHM). However, modeling becomes necessary when more information is needed for more complex functions of the SHM system, such as estimation of remaining life. In addition, suitable models would aid in more accurately identifying and locating damage and in designing the SHM system. Since impedance-based SHM relies on high frequency excitation of the structure using piezoelectric patches, finite element modeling may not be computationally efficient. In this study, the spectral element method (SEM) is used in combination with electric circuit analysis for impedance modeling. SEM more accurately models higher frequency vibrations than finite element methods since the mass is modeled exactly and it incorporates higher order models more easily. Simulations of sensor multiplexing, high frequency response, and the inclusion of damage are presented. Experimental verification is also included.

In order to solve the problem of abnormal structural health monitoring data of large-span bridges, the construction of civil engineering structural health monitoring platform based on intelligent algorithm is proposed. This paper utilizes the Internet of Things RFID technology to deploy health monitoring sensor nodes and process the bridge structural health monitoring data, so as to realize the large-span bridge structural health monitoring. The experimental results show that the structural health monitoring method designed in this paper for large-span bridges is effective, and the accuracy rate between the measured data values and the real values is 90%, which is highly reliable and has certain application value. Conclusion: The designed structural health monitoring method for large-span bridges has a high degree of agreement between the main girder vertical deformation variables and the actual main girder vertical deformation variables at different monitoring points, which proves that the adopted structural health monitoring method for large-span bridges has a good accuracy and has certain application value.