Hwee Kwon Jung

Relative baseline features for impedance-based structural health monitoring

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.

Camera image processing for automated crack detection of pressed panel products (Conference Presentation)

Crack detection on pressed panel during the press forming process is an important step to ensure the quality of panel products. Traditional crack detection technique has been generally performed by experienced human inspectors, which is subjective and expensive. Therefore, the implementation of automated and accurate crack detection is necessary during the press forming process. In this study, we performed an optimal camera positioning and automated crack detection using two image processing techniques with multi-view-camera system. The first technique is based on evaluation of the panel edge lines which are extracted from a percolated object image. This technique does not require a reference image for crack detection. Another technique is based on the comparison between a reference and a test image using the local image amplitude mapping. Before crack detection, multi-view images of a panel product are captured using multiple cameras and 3D shape information is reconstructed. Optimal camera positions are then determined based on the shape information. Afterwards, cracks are automatically detected using two crack detection techniques based on image processing. In order to demonstrate the capability of the proposed technique, experiments were performed in the laboratory and the actual manufacturing lines with the real panel products. Experimental results show that proposed techniques could effectively improve the crack detection rate with improved speed.

Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment

This paper presents the results obtained using time-series-based methods for structural damage assessment. The methods are applied to a wind turbine blade structure subjected to fatigue loads. A 9 m CX-100 (carbon experimental 100 kW) blade is harmonically excited at its first natural frequency to introduce a failure mode. Consequently, a through-thickness fatigue crack is visually identified at 8.5 million cycles. The time domain data from the piezoelectric active-sensing techniques are measured during the fatigue loadings and used to detect incipient damage. The damage-sensitive features, such as the first four moments and a normality indicator, are extracted from the time domain data. Time series autoregressive models with exogenous inputs are also implemented. These features could efficiently detect a fatigue crack and are less sensitive to operational variations than the other methods.

Automatic Crack Detection on Pressed Panels Using Camera Image Processing with Local Amplitude Mapping

Panel crack detection during the manufacturing process is an important step for ensuring the quality in the industry. Traditional crack detection methods are subjective and expensive because they are performed by human inspectors. Therefore, implementation of on-line and precise crack detection is necessary during the panel pressing process. In this paper, two image process based crack detection methods are developed by inspecting panel product images obtained by a regular CCTV camera system. The first technique is based on evaluation of the panel edge lines which are extracted from a percolated object image. This technique does not require a baseline image for crack detection. Another technique is based on the comparison between a base and a test image using the local image amplitude mapping. Experiments are performed in the laboratory and in the actual manufacturing lines. Experimental results demonstrate that the proposed method could effectively detect the panel cracks with improved speed.

A Haptic Approach for Impact Detection on Airplane Wings

This study presents a new sensing paradigm of human and computer collaboration for structural health monitoring. The goal of this study is that, human pilots are able to “feel” wing’s structure responses (especially impacts in this study) and determine impact locations and strength by using a vibro-haptic interface. Both hardware and software components have been developed for this haptic system. L-shape piezoelectric sensor arrays are deployed to measure the acoustic emission data caused by impacts and unique haptic feedback signals are generated after processing the measured data. These haptic signals are wirelessly transmitted to an arm wearable haptic interface, which provides the information on impact locations and intensity. Our experimental results demonstrate that human could detect impact events and judge whether subsequent damage would occur or not by only using haptic interfaces. In addition, human could notice errors such as those caused by algorithm or DAQ failures by feeling haptic feedback signals. Several important aspects of this study, including development of haptic interfaces, human training strategies, and extension of the haptic capability into damage detection are summarized in this paper. doi: 10.12783/SHM2015/20