Various experimental studies have demonstrated that an impedance-based method is an effective means of structural damage detection. Using the self-sensing and active-sensing capabilities of piezoelectric materials, the electromechanical impedance response can be monitored to provide a qualitative indication of the overall health of a structure. In this article, two new signal processing tools for the impedance method are described in order to improve the damage detection capability and to reduce the amount of data to process for structural health assessment. The first approach is to instantaneously correlate the impedance data between different sensor sets, as opposed to be correlated to pre-stored baseline data. Another approach is to use the pre-defined parameter of impedance data to establish a generalized baseline for bolted joint monitoring. These approaches could reduce the number of data sets and could be efficiently used for low-power impedance devices. The proposed signal processing techniques are applied to several experimental structures, and the efficiency in damage detection is demonstrated.

Beamforming or phased array with an array of sensors is an advanced signal processing technique for directional signal transmission or reception. This directionality is achieved by phase shifts of received signals of each sensor for the constructive interference of wavefronts, resulting in the amplification of the signal from a particular direction. In this research, the use of an asymmetric sensor array is proposed to reduce the effects of “spatial aliasing,” which is typically encountered in the structural health monitoring practice when employing phased arrays. In this technique, a sensor array is asymmetrically and closely deployed for beamforming and for robust source localization. This sensor deployment has a great effect on reducing the spatial aliasing errors. Although many advanced signal processing algorithms have been developed in the past, an asymmetric sensor array proposed in this study is used to reduce the spatial aliasing error from the sensor deployment perspective. In order to demonstrate the proposed sensor array technique, several simulation and experimental investigations are carried out, and the performance comparison is made to demonstrate the superior robustness of the asymmetric sensor array.

Crack detection during the manufacturing process of pressed-panel products is an important aspect of quality management. Traditional approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the manufacturing process. In this article, a crack detection technique based on image processing is proposed that utilizes the images of panel products captured by a regular camera system. First, the binary panel object image is extracted from various backgrounds after considering the color factor. Edge lines are then generated from a binary image using a percolation process. Finally, crack detection and localization is performed with a unique edge-line evaluation. In order to demonstrate the capability of the proposed technique, lab-scale experiments were carried out with a thin aluminum plate. In addition, a test was performed with the panel images acquired at an actual press line. Experimental results show that the proposed technique could effectively detect panel cracks at an improved rate and speed. The experimental results also demonstrate that the proposed technique could be an extension of structural health monitoring frameworks into a new manufacturing application.

This article presents a technique for detecting structural impact and damage by integrating passive and active-sensing approaches. An L-shaped piezoelectric sensor array was used to detect and localize impacts by measuring the response of structures. It was found that since this method does not require prior knowledge of structures such as the direction-dependent wave velocity profiles, accurate results could be achieved even on anisotropic structures. This sensor array was then extended to include an active-sensing approach, and the same sensor array was used for damage detection by measuring scattering and reflected waves. A series of experiments was carried out to demonstrate the proposed techniques. The superior capability of the proposed techniques was experimentally demonstrated.

Lamb waves are one of the promising tools to detect damage in aircraft structures. They can be generated in aircraft structures by piezoelectric actuators, propagate a long distance, and be picked up by several types of sensors. The waves can be easily distorted and attenuated by damage. In this study, internal longitudinal damage in a beam structure is experimentally and numerically detected. In order to create longitudinal Lamb waves, a unidirectional piezoelectric actuator is used. Plane strain models and three-dimensional solid models are developed, and dynamic simulations are performed using ABAQUS/Explicit. Lamb wave signals picked up by multiple sensors through the experiment are compared with the simulation results. Two internal notch damages are detected and the damage locations are estimated with the Lamb wave signals.