Silicon-based strain gauge sensors embedded in composite structures for real-time strain and creep analysis

This work shows how the insertion of silicon mechanical sensors into composite structures enables the detection of internal structural variations, and can find applications in process monitoring or structural health monitoring.It includes the description of a novel process for minimally intrusive insertion of sensors inside composites (substrate free transfer printed sensor).Temperature and deformation characterizations reveal a modification of the internal structure of a composite, triggered by a sufficiently high temperature, and linked to the creep phenomenon of the composite's epoxy resin binder.

Wireless Intelligent Sensor and Actuator Network - A Scalable Platform for Time-synchronous Applications of Structural Health Monitoring

Wireless Sensor Network (WSN) is often used for developing Structural Health Monitoring (SHM) application by civil researchers but they do not have much expertise on hardware and network related issues. By providing programming abstractions and hiding low level network issues middleware layer makes it easier to develop an efficient WSN-based SHM application. Service-oriented architecture (SOA) is a popular approach for designing middleware for WSN as it provides flexibility in developing WSN applications by using loosely coupled services. SOA can overcome issues like adaptation, reliability which are usually difficult to deal using other middleware approaches applied for WSN. This paper surveys various middleware approaches for WSN focusing mainly on SOA-based approach. It discusses drawbacks in various middleware approaches and points out design issues that not completely addressed by existing middleware architectures designed for SHM application. An easy-to-use SOA-based middleware, named MidSHM, has been proposed to deal with various SHM application issues such as resource optimization, in-network processing, quality of service, and fault tolerance. Two different application examples enabled by MidSHM are also shown to illustrate its flexibility and usability.

In recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.KeywordsArtificial intelligence (AI)Machine learning (ML)Structural health monitoring (SHM)Explainable artificial intelligence (XAI)

In this paper, time domain data from piezoelectric active-sensing techniques is utilized for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, their ability to provide known repeatable inputs for active-sensing approaches to SHM makes the development of SHM signal processing algorithms more efficient and less susceptible to operational and environmental variability. However, to date, most of these techniques have been based on frequency domain analysis, such as impedance-based or high-frequency response functions-based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or other analysis for damage-sensitive feature extraction. Therefore, this study investigates the use of a time-series predictive model which utilizes the data obtained from piezoelectric active-sensors. In particular, time series autoregressive models with exogenous inputs are implemented in order to extract damage-sensitive features from the measurements made by piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were artificially imposed. The performance of this approach is compared to that of analysis based on frequency response functions and its capability for SHM is demonstrated.

Fiber optic acoustic emission sensor and its applications in the structural health monitoring of CFRP materials

The current FAA (Federal Aviation Administration) and CS (Certification Standard) damage tolerance requirements and their interpretation impact the design and the weight of large areas of civil transport aircraft made of metal. Examples for these areas are specific stiffened panels of the fuselage and the wing. The application of structural health monitoring (SHM) in these areas allows inspections of internal and external structures at small intervals. This improved information compared with the results of traditional inspections, especially about the internal structure, allows a less conservative interpretation of the damage scenarios used in the so‐called damage tolerance analysis. The resulting benefits can be used either for reduction of the maintenance requirements, for an optimization of the design to achieve weight savings or for a combination of both benefits. This article deals with the major aspects of SHM application for improving the design efficiency of metallic fuselage structures of a large civil transport aircraft. The presentation discusses major details such as the general philosophy, possible sensor application, and requirements about the durability of the integrated SHM system. General examples using typical fuselage design features and synthetic operational stresses of the biaxial loaded panels in different fuselage areas are presented. Parametric damage tolerance analyses using fracture mechanics methods show the possible weight savings or the possible reduction of the maintenance program for the defined assumptions, e.g., material of structure, location of sensors and detectable damage size.

Since there is not much research on structural health monitoring (SHM) applications in tall buildings nowadays, this paper gives a proposal of how it can be applied on skyscrapers. Covering the whole process of SHM, this paper focuses more on the diagnostic algorithms, including Structural dynamic index method, Modal parameter identification method Neural network algorithm and Genetic algorithm and how these algorithms can be used in SHM. After introducing the basic process of SHM, an example is given to show how these principles can be applied in this over 400m building. And after all these introductions, a conclusion can be drawn that the structural health monitoring system can be applied properly in tall buildings following the way proposed in this paper.

The load-carrying capability of marine steel structures often suffers degradation and at times structural failure during their service life due to the corrosive sea environment and the extreme wave loads which they experience. Structural integrity is a high priority during asset design and operation to preserve human safety, environment protection, and to maintain operations. Structural Health Monitoring (SHM), a process of deriving the health status and predicting structural damage via sensor-based measurements, data trending and analysis, has been commonly implemented in marine and offshore assets for decades. There are mature commercial SHM products on the market and available international and industrial regulations, guidance and classification rules (such as IMO MSC/Circ. 646, ABS Guide for Hull Condition Monitoring Systems, etc.). With the emergence of the Structural Digital Twin (SDT) concept in the marine industry in the recent years, the application of structural sensors on marine structures and the integration between sensor-based SHM techniques and structural digital twin approaches have attracted more attention. As a digital representation of the physical asset, the structural digital twin typically involves multiple-scale, multiple-physics, and data driven models and simulations. Leveraging the high fidelity full-scale SHM data, the structural digital twin is capable to provide more accurate assessments, predictions, and insights on structural integrity conditions and support the decision-making process regarding asset operation, structural inspection, repairs, and condition-based asset integrity management. This paper reviews the mature and commonly employed SHM sensor techniques (such as electric resistance strain gauge, fiber Bragg grating (FBG) strain gauge, accelerometer, pressure transducer, etc.) and discusses their implementation on marine assets and integration with structural digital twin. The paper elaborates to provide some general guidance on the selection of the structural sensors and their installation, as well as the proper sensor specification, in the context of a structural digital twin implementation. The paper also discusses the required engineering mindsets and skillsets for developing and deploying sensor plans, and for implementing a sensor-based structural digital twin framework. The paper concludes that a successful SHM plan and structural digital twin implementation rely on sensor planning, data acquisition, data processing, analytic models and a proper integration of the data and data analytics. The overall planning and implementation should be governed by the suitability for the business purpose and implementation expectation.

As a typical non-contact sensing and monitoring capability, optical measurements have been gaining tremendous attention recently in structural dynamics identification and health monitoring. This non-contact methodology usually supplies full-field measurement, and is able to out-play traditional transducers such as accelerometers, laser vibrometers and strain gauges. As a new computer vision based approach, 2-D Phase-Based Motion Estimation (PME) is extended in this paper to 3-D applications for structural health monitoring (SHM) purposes. Compared to the 2-D PME, which only captures the in-plane motions on the selected field of view of the camera, 3-D PME removes these restrictions and estimates the out-of-plane motion partially if the required conditions are met for the proposed algorithm. The modal information such as natural frequencies of the test wind turbine blade are extracted and compared with the results obtained via 2-D PME, as well as other conventional measurement approaches. With all the modes decoupled on the testing turbine blade, a contrived damage is added to the structure, and the 3-D PME proposed in this paper successfully extracts the modal frequencies for both conditions, baseline and damaged, and therefore detects damages in a non-contact and full-scale fashion.

Development of optical fiber sensors for the Structural Health Monitoring in Aeronautical Composite Structures = Desarrollo de sensores de fibra óptica para su aplicación a la monitorización de la integridad estructural en estructuras aeronáuticas de material compuesto

Time reversal active sensing using Lamb waves is investigated for health monitoring of a composite structure. In the present work experiments were conducted on a glass/epoxy composite plates to study the time reversal behavior of A0 and S0 Lamb wave modes. PZT wafer sensors were surface bonded to the structure for generation and reception of the Lamb wave modes. Time reversal experiments were further carried out on carbon/epoxy composite t-pull specimen. The specimen was subjected to a tensile loading in a Universal testing machine. The PZT sensor measurements were carried out for healthy and also during different stages of delamination due to tensile loading. Time reversal of A0 and S0 mode was applied for both healthy and delaminated composite structures. In this work, the shape of the time reversed Lamb wave in the presence of delamination was studied experimentally. This study aims in showing the effectiveness of Lamb wave time reversal for damage detection in health monitoring applications. Lamb wave based NDE methods are widely used for damage detection in engineering structures, especially aerospace, civil and marine structures. The existence of damage in a structure is generally traced by comparing the time-domain traveling wave response of the structure at its present state with a base-line response. Any fluctuation from the base-line response is correlated to the damage location through the time of arrival of the new peaks (scattered waves). Therefore by employing the wave based methods, the presence of damage in a structure is detected by looking at the wave parameters affected by the damage. The wave parameters that are commonly used for damage detection are the parameters representing attenuation and reflection of waves due to damage, mode conversion etc. In addition the ability of Lamb waves to interrogate large and complex structure quickly and the generation (and reception) of Lamb waves using embedded or surface bonded PZT wafers makes them suitable for Structural Health Monitoring (SHM) applications. The major problem of employing Lamb waves for SHM is being multimodal and dispersive in nature which makes it more difficult to analyze and interpret the experimental signals. The other major concern is the effect of damage on the Lamb waves are small comparable to the effects due to geometry of the finite structure which causes dispersion and scattering of waves. To overcome the above problems, advanced signal processing techniques based on time-frequency analysis have been employed to extract the useful information from the Lamb waves. But signal processing techniques post process the wave response and remove the dispersion effects. Therefore a new approach is required to reduce the dispersion effects of Lamb waves before applying signal processing techniques and one such method is the time reversal method.

Design and Analysis of MEMS Comb Drive Capacitive Accelerometer for SHM and Seismic Applications

This report discusses the guided Lamb wave sensing using polarization-maintaining (PM) fiber Bragg grating (PM-FBG) sensor. The goal is to apply the PM-FBG sensor system to composite structural health monitoring (SHM) applications in order to realize directivity and multi-axis strain sensing capabilities while using reduced number of sensors. Comprehensive experiments were conducted to evaluate the performance of the PM-FBG sensor in a composite panel structure under different actuation frequencies and locations. Three Macro-Fiber-Composite (MFC) piezoelectric actuators were used to generate guided Lamb waves and they are oriented at 0, 45, and 90 degrees with respect to PM-FBG axial direction, respectively. The actuation frequency was varied from 20kHz to 200kHz. It is shown that the PM-FBG sensor system is able to detect high-speed ultrasound waves and capture the characteristics under different actuation conditions. Both longitudinal and lateral strain components in the order of nano-strain were determined based on the reflective intensity measurement data from fast and slow axis of the PM fiber. It must be emphasized that this is the first attempt to investigate acousto-ultrasonic sensing using PM-FBG sensor. This could lead to a new sensing approach in the SHM applications.

Quasi-phase-matched nonlinear Lamb waves in composite laminates for material degradation monitoring