Vibration Analysis of Cantilever Beams Using Deep Learning Enhanced Photodiode Non-Uniformity
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 12 |
| Year of Publication : 2025 |
| Authors : Honey Devassy, L.D. Vijay Anand, Hepsiba D |
10.14445/23488549/IJECE-V12I12P115
How to Cite?
Honey Devassy, L.D. Vijay Anand, Hepsiba D, "Vibration Analysis of Cantilever Beams Using Deep Learning Enhanced Photodiode Non-Uniformity," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 12, pp. 177-188, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P115
Abstract:
Conventional vibration monitoring techniques for cantilever beams suffer from several limitations, including physical contact requirements, installation complexity, degradation over time, and susceptibility to environmental noise. Photodiodes, while commonly used in optical sensing, are typically assumed to exhibit uniform responsivity. Additionally, traditional approaches offer limited scalability for real-time, non-invasive, and predictive maintenance solutions. This paper presents a novel vibration analysis technique that exploits the non-uniform spectral responsivity of photodiodes to detect beam oscillations without physical contact. When a vibrating cantilever beam reflects a laser spot across the photodiode surface, spatial variations in light incidence produce voltage fluctuations that are recorded using a digital storage oscilloscope. Experiments conducted on different cantilever beams reveal that the proposed method accurately determines natural frequencies. To enhance diagnostic accuracy, the voltage signals are processed using a Deep Learning Model, Sentiment Cross-Fusion Network (SCFN), optimized with the Improvised Arctic Fox Algorithm (IAFA). Among competing models, the sentiment cross-fusion network achieved the highest classification accuracy of 0.90%. The improvised arctic fox algorithm further improved prediction performance, achieving 92.1% accuracy, with the lowest error values of root mean square error (0.10) and mean absolute error (0.07). The proposed framework demonstrates excellent potential for real-time, scalable, and accurate structural health monitoring in civil and industrial applications, although considerations like photodiode alignment and active area limitations must be addressed for broader deployment.
Keywords:
Vibration Analysis, Cantilever Beam, Photodiode Non-Uniformity, Deep Learning, Optimization.
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