Comprehensive Study of Vibration Analysis Methods for Fault Detection and Condition Monitoring

Abstract

Vibration analysis has emerged as a critical tool in monitoring, diagnosing, and predicting the health of mechanical systems across industries. This study explores various vibration analysis methods, including time-domain, frequency-domain, and time-frequency-domain techniques, focusing on their application to fault detection and condition monitoring in rotating machinery, structures, and vehicles. Advanced signal processing methods, such as Fast Fourier Transform (FFT), Wavelet Transform (WT), and machine learning-based approaches, are discussed for their potential to improve diagnostic accuracy. Experimental results highlight the strengths and limitations of each method, providing insights into their suitability for specific applications. The study emphasizes the importance of integrating vibration analysis with emerging technologies like artificial intelligence and the Internet of Things (IoT) to enable real-time, predictive maintenance solutions. This research aims to guide practitioners in selecting appropriate vibration analysis techniques and to advance the development of efficient, reliable monitoring systems.

Cite as:

S Mulani. (2025). Comprehensive Study of Vibration Analysis Methods for Fault Detection and Condition Monito. Advancement in Mechanical Engineering and Technology, 8(1), 35–39. 

https://doi.org/10.5281/zenodo.14844184