Diagnosing the Defects of the Gears of the Gearbox Using Vibration Analysis and Validating it by the Graphs of the CMM Machine
محورهای موضوعی : journal of Artificial Intelligence in Electrical EngineeringMohammad Esmaeil akbari 1 , alireza mangouri 2 , سجاد عطازاده 3
1 - Director-in-Charge
2 - Department of Mechanical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran
3 -
کلید واژه: Gear Fault Diagnosis, Vibration Analysis, Artificial Neural Network (ANN), Machine Learning (ML), Industry 5.0, Signal Processing,
چکیده مقاله :
This study focuses on diagnosing gear defects in the S5-380 transmission using advanced vibration analysis and validating findings through the HÖFLER Gear Testing Machine. The research highlights key faults such as backlash, eccentricity, and shaft deflection identified via vibration measurements and confirmed using detailed gear profile and lead inspections. The vibration analysis employed V-SAM Vibrometer and V-SAM-Web Soft to detect critical parameters, including Gear Mesh Frequency (GMF) and Gear Hunting Tooth Frequency (GHTF). Results demonstrated the robustness of vibration analysis as an efficient, cost-effective alternative to Coordinate Measuring Machines (CMM) for end-of-line quality control. Furthermore, integrating vibration analysis with CMM-based validation provided precise fault identification, enabling targeted maintenance and enhanced gearbox performance. The findings underscore the potential of transitioning to AI-driven diagnostic systems in Industry 5.0 for real-time, scalable, and intelligent gearbox monitoring. This approach offers significant implications for improving durability, quality, and operational efficiency in gear manufacturing.
This study focuses on diagnosing gear defects in the S5-380 transmission using advanced vibration analysis and validating findings through the HÖFLER Gear Testing Machine. The research highlights key faults such as backlash, eccentricity, and shaft deflection identified via vibration measurements and confirmed using detailed gear profile and lead inspections. The vibration analysis employed V-SAM Vibrometer and V-SAM-Web Soft to detect critical parameters, including Gear Mesh Frequency (GMF) and Gear Hunting Tooth Frequency (GHTF). Results demonstrated the robustness of vibration analysis as an efficient, cost-effective alternative to Coordinate Measuring Machines (CMM) for end-of-line quality control. Furthermore, integrating vibration analysis with CMM-based validation provided precise fault identification, enabling targeted maintenance and enhanced gearbox performance. The findings underscore the potential of transitioning to AI-driven diagnostic systems in Industry 5.0 for real-time, scalable, and intelligent gearbox monitoring. This approach offers significant implications for improving durability, quality, and operational efficiency in gear manufacturing.
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