Structural Health Monitoring Using Sensors with Application of Wavelet Analysis

Abstract

Abstract. Structural Health Monitoring (SHM) establishes a damage detection system to maintain safety in the current structure. Owners and maintenance managers are looking for cost-effective and dependable inspection and monitoring solutions to ensure the safety and reliability of capital-intensive assets. A longer overhaul time is envisaged with today's software technology and design codes. The visualization aims to put advanced technology into practice to provide consumers and the government with value-added services. Meanwhile, the wavelet transforms, a signal processing technique based on a windowing approach using enlarged 'scaled' and shifted wavelets, is being applied in various industries. By bypassing many of the restrictions of the Fourier transform (FT), the wavelet transform has proven to be effective in SHM systems. Structures tend to get damaged in situations such as accidental fire, aggregate contraction, salinity exposure, corrosion due to bacterial influence, physical and material damage. Also, structures tend to lose their tensile strength when exposed to long-term factors such as moisture, heat, rains, storms, etc. Structural Health Management plays a vital role here to monitor the health conditions of structures to prevent any loss. To stand up for this need, it is imminent to provide a safe structure for people to ply through. The proposed methodology shows a clear picture of how to assess any structure condition at any time and gives a clear view of its current stature on whether it is damaged. Hence in this article, the behavior of the structure is assessed using wavelet transformation. The hardware configurations, including the MSP430FR6989 microcontroller with TDC1000-TDC7200EVM, are embedded with the aggregate, making it smart enough to detect the defects through the software interpretation with a signal processing toolbox of MATLAB coding.