A novel deep convolutional image-denoiser network for structural vibration signal denoising. (January 2023) - Xiong, Qingsong; Xiong, Haibei; Yuan, Cheng; Kong, Qingzhao
Abstract: Vibration-based approach is of great importance for structural health monitoring and condition assessment, while inevitable noise existing in field measurement casts great obstacles in corresponding data-driven analysis. It has been a stringent prerequisite to develop effective methods to denoise vibration signal. Hence, a novel denoising approach based on deep convolutional image-denoiser networks (DCIMN) is proposed in this study, the methodology and architecture of which are elaborated. Specified avenues with novelties including noise injection in training labeling, dimension expansion in feature extraction, and optimizer embedding in encoder–decoder are utilized to enhance the denoising performance. Measured vibration data from Shanghai Tower is allocated for validation, based on which modal identifications are also conducted. Detailed evaluation confirms its powerful capability and efficiency in denoising signal. Demanding no prior information of input signal, the proposed method performs vibration signal denoising in an intelligent way, which demonstrates a vast prospect in engineering practice. Highlights: A novel deep convolutional image-denoiser network for non-stationary structural vibration signal denoising is proposed. The whole denoising processing demands no prior knowledge of input signals or any human intervention. Noise injection dimension expansion and optimizer embedding strategies are utilized to enhance the denoising performance. Both linear parameters evaluation and non-linear systematic analysis validate the effectiveness and superiority. Field measurements of Shanghai Tower under three typhoon events are allocated for validations.