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10. Signal Processing in Real-Life Structural Health Monitoring: MATLAB and Python Implementation

  • 2025
  • OriginalPaper
  • Chapter
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Abstract

This chapter delves into the critical role of signal processing in structural health monitoring (SHM), focusing on the practical implementation of techniques using MATLAB and Python. It begins by discussing the importance of continuous measurement and the various sensing technologies employed to capture structural responses. The evolution of digital signal processing and its applications across multiple engineering fields is explored, highlighting the necessity of advanced computational tools. The chapter provides an in-depth comparison of MATLAB and Python for signal processing tasks, including file management, signal generation, noise addition, filtering, and frequency domain analysis. It demonstrates the use of Fast Fourier Transform (FFT) for frequency analysis and the implementation of digital low-pass filters to remove high-frequency noise. The chapter also covers cross-correlation analysis for identifying structural similarities and statistical analysis for feature extraction. Real-life applications are illustrated through experimental analysis of a supported beam, showcasing the practical utility of the discussed techniques. The comparative analysis of MATLAB and Python provides valuable insights into their respective strengths and limitations, making this chapter an indispensable resource for professionals and researchers in the field of SHM.

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Title
Signal Processing in Real-Life Structural Health Monitoring: MATLAB and Python Implementation
Authors
Chandrabhan Patel
Naman Garg
S. K. Panigrahi
Vikesh Kumar
Copyright Year
2025
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-8975-7_10
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