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.

This article investigates the role of ambient temperature in causing changes to the structural wave propagation, as sensed by piezoelectric transducers, in a newer perspective. A novel approach is proposed to compensate the influence of temperature on piezo-sensor response using both analytical models and numerical simulations. Parametric studies using numerical simulations for plates with surface-mounted piezoelectric transducers establish linear functional relationship between change in sensor signals and specific combination of material properties, within certain temperature range. A numerical temperature compensation model is developed based on this functional relationship to reconstruct piezosensor signals at elevated temperatures. Matching pursuit-based signal analysis and reconstruction schemes are used in this study. Practical efficacy of the compensation model is tested for metallic structures with both simple and complex geometries. Model-based reconstruction of first wave packets in the sensor signals is found to match quite well with the experimental measurements. Performance of the proposed compensation model is also found to be at par with the existing state-of-art temperature compensation methods. A very limited set of baseline sensor data is required to estimate unknown model parameters, making this approach to be efficient and practically useful. The output of the compensation model is also used to obtain an accurate estimate of damage location in a structure under varying ambient temperature environments.

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Next generation technology of integrated health management systems for air-transportation structures will utilize SHM methods in combination with simulation techniques for the prediction of structural degradation induced by adverse events such as impacts. The contribution focuses on the development of an advanced real-time monitoring system for impact loads using passive sensing networks. Starting from the fundamental approach of real-time monitoring based on system identification models, problems of model order, signal conditioning and efficient model training will be addressed. Finally, the load monitoring system is interactively linked to a damage prediction module based on numerical failure analysis employing composite failure criteria. This module allows a prediction of impact-induced damage after detection of any adverse impact event making information available on developing degradation at the earliest possible state.

This paper presents a novel model-assisted integrated diagnostics for structural health monitoring. Acousto-ultrasonic Lamb waves are propagated through a structure induced and sensed by an active piezoelectric sensor network. The damage is detected by comparing current sensor signals (with damage) with baseline signals (without damage). Diagnostic algorithm interprets changes in the signals to detect and localize damage. Typically the accuracy of damage localization depends on a priori knowledge of the velocity of Lamb waves. The estimation of Lamb wave velocity for complex structures is very challenging since analytical relations only exist for structures with simple geometries. Hence, a numerical tool based on spectral element method has been developed and employed to simulate acousto-ultrasonic wave propagation in structures. This tool generates an accurate velocity profile of Lamb waves propagating through a complex structure, which is used for offline training of the diagnostic algorithm. In order to achieve accurate diagnostics in varying temperature environments, the diagnostic algorithm has been integrated with a model to compensate for the effect of change in ambient temperature on sensor signals. Numerical tests are carried out to determine the performance of the integrated diagnostics for an aluminum stiffened panel with an open crack at different ambient temperatures. The results demonstrate that the proposed model-assisted integrated diagnostics has the capability of providing an accurate localization of damage in complex structures.Nomenclature ( ) = sensor signal ( , ) = short-time Fourier transform (STFT) of sensor signal ( ) = distance between pixel and actuator of ℎ actuator-sensor pair = Lamb wave velocity of S 0 mode in direction of path connecting actuator and pixel = distance between pixel and sensor of ℎ actuator-sensor pair = Lamb wave velocity of S 0 mode in direction of path connecting pixel and sensor = time of flight (ToF) of the scatter signal corresponding to pixel and ℎ actuator -sensor pair = intensity of pixel from all actuator-sensor pairs = stress tensor = strain tensor = body force vector = density = displacement vector = electric displacement vector = electric field vector = material stiffness matrix at constant electric field = piezoelectric coupling coefficients for stress-charge form ∈ = electric permittivity of piezoelectric material at constant strain