Compressive Sensing Approaches for Condition Monitoring and Laser-scanning Based Damage Visualization

Abstract

Condition monitoring (CM) and Non-destructive testing (NDT) encounter a big data problem because they require to continuously measure vibration or wave data with a high sampling rate. In this study, compressive-sensing approaches for both condition monitoring and non-destructive testing are proposed to efficiently handle a huge amount of data and to improve the damage-detection capability of the current process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For CM experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. Optimal signal features were then extracted without the reconstruction process and were used to detect and classify damage. Also Non-destructive testing was conducted by using compressed scanning with a Laser Doppler Vibrometer (LDV) system equipped with a mirror tilting device. Wave fields were obtained by scanning through a random and compressive pattern in the spatial domain and full wave fields were reconstructed from the compressively measured data. The damage region was then identified and visualized using wavenumber based signal processing. Experimental results showed that the proposed method could effectively improve the data-processing speed and the detection accuracy of condition monitoring and non-destructive testing.