Noise is unavoidable and ever-present in measurements. As a result, signal denoising is a necessity for many scientific and engineering disciplines. In particular, structural health monitoring applications aim to detect often weak anomaly responses generated by incipient damage (such as acoustic emission signals) from background noise that contaminates the signals. Among various approaches, stochastic resonance has been widely studied and adopted for denoising and weak signal detection to enhance the reliability of structural heath monitoring. On the other hand, many of the advancements have been focused on detecting useful information from the frequency domain generally in a postprocessing environment, such as identifying damage-induced frequency changes that become more prominent by utilizing stochastic resonance in bistable systems, rather than recovering the original time domain responses. In this study, a new adaptive signal conditioning strategy is presented for on-line signal denoising and recovery, via utilizing the stochastic resonance in a bistable circuit sensor. The input amplitude to the bistable system is adaptively adjusted to favorably activate the stochastic resonance based on the noise level of the given signal, which is one of the few quantities that can be readily assessed from noise contaminated signals in practical situations. Numerical investigations conducted by employing a theoretical model of a double-well Duffing analog circuit demonstrate the operational principle and confirm the denoising performance of the new method. This study exemplifies the promising potential of implementing the new denoising strategy for enhancing on-line acoustic emission-based structural health monitoring.
Jinki Kim, Ryan L. Harne, and K. W. Wang, "Signal denoising using stochastic resonance and bistable circuit for acoustic emission-based structural health monitoring," Proc. SPIE 10170, Health Monitoring of Structural and Biological Systems 2017, 1017016 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 28, 2017; Published: 5 April 2017); https://doi.org/10.1117/12.2258733.
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