The goal of this article is to detect structural damage in the presence of operational and environmental variations using vibration-based damage identification procedures. For this purpose, four machine learning algorithms are applied based on the auto-associative neural network, factor analysis, Mahalanobis distance, and singular value decomposition. A base-excited three-story frame structure was tested in laboratory environment to obtain time-series data from an array of accelerometers under several structural state conditions. Tests were performed with varying stiffness and mass conditions with the assumption that these sources of variability are representative of changing operational and environmental conditions. Damage is simulated through nonlinear effects introduced by a bumper mechanism that induces a repetitive, impact-type nonlinearity. This mechanism intends to simulate the cracks that open and close under dynamic loads or loose connections that rattle. The unique contribution of this study is a direct comparison of the four proposed machine learning algorithms that have been reported as reliable approaches to separate structural conditions with changes resulting from damage from changes caused by operational and environmental variations.

This paper presents an overview and recent advances in impedance-based structural health monitoring. The basic principle behind this technique is to apply high-frequency structural excitations (typically greater than 30 kHz) through surface-bonded piezoelectric transducers, and measure the impedance of structures by monitoring the current and voltage applied to the piezoelectric transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred. An experimental study is presented to demonstrate how this technique can be used to detect structural damage in real time. Signal processing methods that address damage classifications and data compression issues associated with the use of the impedance methods are also summarized. Finally, a modified frequency-domain autoregressive model with exogenous inputs (ARX) is described. The frequency-domain ARX model, constructed by measured impedance data, is used to diagnose structural damage with levels of statistical confidence.

This paper presents a piezoelectric sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric materials used for sensors and actuators in structural health monitoring (SHM) applications. The sensor/actuator self-diagnostic procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is a critical component to successfully complete the SHM process with large numbers of active sensors typically installed in a structure. The premise of this procedure is to track the changes in the capacitive value of piezoelectric materials resulting from the degradation of the mechanical/electrical properties and its attachment to a host structure, which is manifested in the imaginary part of the measured electrical admittances. This paper concludes with an experimental example to demonstrate the feasibility of the proposed procedure.

Self-sensing actuation allows a single piezoelectric (PZT) element to be simultaneously used as both a sensor and an actuator. A specially designed electric circuit, referred to as a bridge circuit, is required to realize the concept. However, precise equilibrium of the bridge circuit is extremely difficult to obtain because of the continuous changes in environmental conditions. In this study, the effects of an unbalanced bridge circuit are analytically and experimentally evaluated in an attempt to quantify the variations in the PZT capacitance in terms of performances in vibration testing and control. Once the dynamic characteristics of self-sensing actuation are identified and understood, methods for improving the system’s performance are developed by utilizing capacitors in series and in parallel with the PZT patch. The analytical and experimental results clearly indicate that the new design scheme increases the stability of the system. However, the increase in stability comes at the cost of the increase in the power required for the control system.

This paper describes the use of wavenumber filtering for damage detection with a signal-frequency standing wave excitation on composite structures. Using a single, fixed frequency excitation from a mounted piezoelectric transducer, the full steady-state wavefield could be obtained using a Laser Doppler Vibrometer with a mirror-tilting device. After completing the scanning, a wavenumber filtering is applied to determine dominant wavenumber components of the measured wavefield, which could be used for indicative of structural damage. Mapping processes based on local wavenumber filtering is then carried out for damaged area visualization. Also introduced are the comparison of two methods for damage identification and visualization: the local wavenumber mapping and acoustic wavenumber spectroscopy. To demonstrate the proposed techniques, several experiments are performed on composite structures with different types of damage, including debonding and delamination on composite plates. The results demonstrate that the techniques are very effective in localizing damage with the potential for the quick inspection of a variety of composite structural components.