The reliability of gearboxes, particularly those with spur gear arrangements, is critical in various industrial applications, as they are subjected to varying load conditions that can lead to wear, failure, and costly downtime. This paper presents an experimental investigation and vibration-based condition analysis of a spur gear gearbox under varying load conditions. The primary objective is to assess the impact of different loading scenarios on the gearbox's vibration characteristics and develop strategies for early fault detection. Using advanced vibration analysis techniques such as Fast Fourier Transform (FFT), wavelet transform, and machine learning models, we analyze vibration signals collected from the gearbox under light, medium, and heavy load conditions. The results show that vibration patterns can be effectively used to identify gear faults and predict potential failures. By integrating machine learning-based predictive maintenance models, the study demonstrates how vibration data can be utilized to enhance the reliability and longevity of gearboxes. The findings provide valuable insights into vibration-based condition monitoring, offering a proactive approach to maintaining gearbox performance and reducing unplanned downtime in industrial environments.
Keywords: Vibration analysis, gearbox, spur gear arrangement, varying load conditions, condition monitoring, fault detection, Fast Fourier Transform (FFT), wavelet transform, machine learning, predictive maintenance, reliability, early failure prediction, industrial applications.
How to cite this article: Suryavanshi S, Rupesh PJ, Hole JA, Navagale NS. Experimental investigation and vibration-based condition analysis for varying load of gear box with spur gear arrangement to increase reliability. Int J Drug Deliv Technol. 2026;16(7s): 278-294; DOI: 10.25258/ijddt.16.7s.32
Source of support: Nil.
Conflict of interest: None