Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Krakow, Poland
Dr. Adam Martowicz
Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Krakow, Poland
We present an overview of research developments related to the nonlinear vibroacoustic modulation technique used for structural damage detection. The method of interest is based on nonlinear interactions of a low-frequency pumping wave and a high-frequency probing wave. These two waves are introduced to monitored structures simultaneously. Then the presence of damage is exhibited by additional frequency components that result from nonlinear damage-wave interactions. A vast amount of research has been performed in this area over the last two decades. We aim to present the state-of-the-art of these developments. The major focus is on monitoring approaches, modeling aspects, actuation/sensing, signal processing, and application examples.
The work discusses the effectiveness of the electromechanical impedance-based Structural Health Monitoring system for damage detection in a laboratory test stand – a bolted pipeline section. The impedance-based system developed by the authors and equipped with 12 piezoelectric transducers was used to acquire the data. Different damage scenarios related to loosened bolts and measurement configurations are analysed. Both point and transfer frequency response functions are applied to deterministic and stochastic damage metrics, which are used to assess the condition of the monitored structure. The thresholds levels are proposed for different measurement configurations. Moreover, the authors discuss the perspective to determine the size and localisation of a damage. As confirmed in the work, the stochastic damage metric, preferably applied with a transfer frequency response, is found as of the most practical significance for the tested structure. The carried out analyses consider an outlier removal technique, which becomes necessary to prevent from errors due to temporary variation of the acquired characteristics after initialisation of the measurements.
1 October 2016 in Mechanical Systems and Signal Processing