Fotis Kopsaftopoulos

Damage detection sensitivity characterization of acousto-ultrasound-based structural health monitoring techniques:

Reliability quantification is a critical and necessary process for the evaluation and assessment of any inspection technology that may be classified either as a nondestructive evaluation or structural health monitoring technique. Based on the sensitivity characterization of nondestructive evaluation techniques, appropriate processes have been developed and established for the reliability quantification of their performance with respect to damage/flaw detection in materials or structures. However, in the case of structural health monitoring methods, no such well-defined and general applicable approaches have been established for neither active nor passive sensing techniques that allow for their accurate reliability quantification. The objective of this study is to characterize the sensitivity of active-sensing acousto-ultrasound-based structural health monitoring techniques with respect to damage detection, as well as to identify the parameters that influence their sensitivity. With such an understanding, it is believed that adequate quantitative methods could then be established to enable the practical use of acousto-ultrasound structural health monitoring methods in the aerospace and mechanical engineering communities. In order to evaluate the sensitivity of a pre-selected active-sensing acousto-ultrasound structural health monitoring system, both numerical simulations and experiments were performed on 30 aluminum coupons each outfitted with a pair of lead zirconate titanate piezoelectric sensors/actuators. A damage index versus damage size relationship was investigated numerically and experimentally to assess the applicability of the traditional nondestructive evaluation linear regression framework for probability of detection for an active-sensing structural health monitoring system. The results of the study show that the position of each sensor–actuator pair with respect to a known damage location and the damage growth pattern are the two most critical parameters influencing the reliability of the same structural health monitoring system applied to identical structural components under the same environmental conditions.

Adhesive bond-line degradation detection via a cross-correlation electromechanical impedance–based approach

This article describes how piezoelectric transducers embedded in the adhesive bond-line of lap-joints can be used to effectively monitor structural integrity. Various lap-joint coupons with embedded piezoelectric transducers were manufactured with and without artificial contamination at the bond-line and tested statically and cyclically. A novel scheme based on the electromechanical impedance response of the transducer was implemented to predict the failure of the tested lap-joint samples. The results from the mechanical testing indicated that monitoring the transducer’s electromechanical impedance is an effective way of predicting the failure of the bond-line. Specifically for static tests, local damage to the bond-line was consistently detected at approximately 84% of the failure load for transducers located at the center of the bond-line, whereas for transducers embedded near the edge of the bond-line, the failure of the adhesive was detected at 60% of the failure load. Moreover, preliminary fatigue tests showed that significant changes in the electromechanical impedance signals were apparent starting at 60% of the life of the bond-line. In addition to the mechanical testing, the effectiveness of the proposed electromechanical impedance–based scheme was investigated by means of a three-dimensional finite element model corresponding to the specific coupon geometry tested and through a two-dimensional analytical solution.

Statistical Time Series Methods for Vibration Based Structural Health Monitoring

Statistical time series methods for vibration based structural health monitoring utilize random excitation and/or vibration response signals, statistical model building, and statistical decision making for inferring the health state of a structure. This includes damage detection, identification (including localization) and quantification. The principles and operation of methods that utilize the time or frequency domains are explained, and they are classified into various categories under the broad non-parametric and parametric classes. Representative methods from each category are outlined and their use is illustrated via their application to a laboratory truss structure.

Vibration-Based Structural Damage Detection and Precise Assessment via Stochastic Functionally Pooled Models

This work aims at the precise assessment of a recently introduced method that, in addition to damage detection, allows for complete and accurate damage identification (localization) and magnitude estimation. The method is based on Vector–dependent Functionally Pooled (VFP) models and is capable of offering an effective and precise solution in a unified framework. The effectiveness of the method is experimentally assessed via its application to a prototype GARTEURtype laboratory scale aircraft structure.

A vibration model residual-based sequential probability ratio test framework for structural health monitoring

The goal of this study is the introduction and experimental assessment of a sequential probability ratio test framework for vibration-based structural health monitoring. This framework is based on a combination of binary and multihypothesis versions of the statistically optimal sequential probability ratio test and employs the residual sequences obtained through a single stochastic time series model of the healthy structure. The full list of properties and capabilities of the sequential probability ratio test is for the first time presented and explored in the context of vibration-based damage diagnosis. The approach postulated in this framework is shown to achieve early and robust damage detection, identification (classification), and quantification based on predetermined sampling plans, which are both analytically and experimentally compared and assessed. The framework’s performance is determined a priori via the use of the analytical expressions of the operating characteristic and average sample number functions in combination with baseline data records. It is shown to require, on average, a minimal number of signal samples in order to reach a decision compared to fixed sample size most powerful tests. The effectiveness of the proposed approach is validated and experimentally assessed via its application to a lightweight aluminum truss structure.

Recent advancements and vision toward stretchable bio-inspired networks for intelligent structures

Significant progress has recently been achieved in structural health monitoring, maturing the technology through quantification, validation, and verification to promote implementation and fielding of SHM. In addition, there is ongoing work seeking to detect damage precursors and to deploy structural health monitoring systems over large areas, moving the technology beyond hot-spot monitoring to global state sensing for full structural coverage. A large number of small sensors of multiple types are necessary in order to accomplish the goals of structural health monitoring, enabling increased sensing capabilities while reducing parasitic effects on host structures. Conventional sensors are large and heavy, adding to the weight of a structure and requiring physical accommodation without adding to and potentially degrading the strength of the overall structure. Increased numbers of sensors must also be deployed to span large areas while maintaining or increasing sensing resolution and capabilities. Traditionally, these sensors are assembled, wired, and installed individually, by hand, making mass deployment prohibitively time consuming and expensive. In order to overcome these limitations, the Structures and Composites Lab at Stanford University has worked to develop bio-inspired microfabricated stretchable sensor networks. Adopting the techniques of complementary metal-oxide semiconductor and microelectromechanical system fabrication, new methods are being developed to create integrated networks of large numbers of various micro-scale sensors, processors, switches, and all wiring in a single fabrication process. Then the networks are stretched to span areas orders of magnitude larger than the original fabrication area and deployed onto host structures. The small-scale components enable interlaminar installation in laminar composites or adhesive layers of built-up structures while simultaneously minimizing parasitic effects on the host structure. Additionally, data processing and interpretation capabilities could be embedded into the network before material integration to make the material truly multifunctional and intelligent once fully deployed. This article reviews the current accomplishments and future vision for these systems in the pursuit of state sensing and intelligent materials for self-diagnostics and health monitoring.

Identification of Stochastic Systems Under Multiple Operating Conditions: The Vector Dependent FP-ARX Parametrization

The problem of identifying stochastic systems under multiple operating conditions, by using excitation-response signals obtained from each condition, is addressed. Each operating condition is characterized by several measurable variables forming a vector operating parameter. The problem is tackled within a novel framework consisting of postulated vector dependent functionally pooled ARX (VFP-ARX) models, proper data pooling techniques, and statistical parameter estimation. Least squares (LS) and maximum likelihood (ML) estimation methods are developed. Their strong consistency is established and their performance characteristics are assessed via a Monte Carlo study

Bondline Integrity Monitoring of Adhesively Bonded Structures via an Electromechanical Impedance Based Approach

Bondline integrity is still one of the most critical concerns in the design of aircraft structures up to date. Due to the lack of confidence on the integrity of adhesive bondlines both during fabrication and service, the industry standards and regulations require assembling the composites using the inefficient “black-aluminum” approach, i.e. drill holes and use fasteners. Furthermore, current state-of-the-art nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques are incapable of offering mature solutions on the issue of bondline integrity monitoring. Therefore, the objective of this work is to investigate the feasibility of embedding piezoelectric sensors into adhesively bonded joints to detect bondline integrity degradation. The proposed method employs an electromechanical-impedance (EMI) based diagnostic approach. This approach is based on the use of (i) micro-sensors embedded inside the adhesive leaving a minimal footprint on the material, (ii) numerical modeling of the EMI spectrum of the adhesive bondline, (iii) EMI based diagnostic algorithms for monitoring the bondline integrity, and (iv) the experimental assessment via adhesively bonded lap joints in static (varying loads) environment. The obtained results demonstrate the potential of the approach in providing increased confidence on the use of bonded joints for aerospace structures. doi: 10.12783/SHM2015/26

Experimental Identification of Structural Dynamics and Aeroelastic Properties of a Self-sensing Smart Composite Wing

Self-sensing intelligent composite materials with state-sensing and awareness capabilities constitute the future of aerospace structures. The objective of this work is to develop technologies that will lead to the next generation of intelligent aerospace structures that can sense the environmental conditions and structural state, effectively interpret the sensing data to achieve real-time state awareness, and employ appropriate self-diagnostics under varying operational environments. In this paper, the design, integration, and experimental identification of the structural dynamics and aeroelastic properties are presented for an intelligent composite UAV wing. Bio-inspired stretchable sensor networks, including integrated piezoelectric, strain, and temperature sensors are monolithically embedded in the composite layup to provide the sensing capabilities. Stochastic signal processing and identification techniques are employed in order to accurately interpret the sensing. The experimental evaluation and assessment is demonstrated via a series of wind tunnel experiment under varying angles of attack and airflow velocities for the identification of the coupled airflowstructural dynamics and strain distribution. The obtained results demonstrate the successful integration of the micro-fabricated stretchable sensor networks with the composite wing, as well as the effectiveness of the stochastic data interpretation approaches. This study constitutes a significant step in proving the integration potential of the approach for the next generation of fly-by-feel UAVs. doi: 10.12783/SHM2015/163

A stochastic global identification framework for aerospace structures operating under varying flight states

Abstract In this work, a novel data-based stochastic “global” identification framework is introduced for aerospace structures operating under varying flight states and uncertainty. In this context, the term “global” refers to the identification of a model that is capable of representing the structure under any admissible flight state based on data recorded from a sample of these states. The proposed framework is based on stochastic time-series models for representing the structural dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed, angle of attack, altitude and temperature, forming a flight state vector. The method’s cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. This is achieved via the use of functional data pooling techniques for optimally treating – as a single entity – the data records corresponding to the various flight states. In this proof-of-concept study the flight state vector is defined by two variables, namely the airspeed and angle of attack of the vehicle. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the identification of a stochastic global VFP model via appropriate parameter estimation and model structure selection methods. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the wing’s aeroelastic response under the admissible flight states via a minimum number of estimated parameters compared to standard identification approaches. The obtained results demonstrate the high accuracy and effectiveness of the proposed global identification framework, thus constituting a first step towards the next generation of “fly-by-feel” aerospace vehicles with state awareness capabilities.