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Spur Gear Fault Detection Using Design of Experiments and Support Vector Machine (SVM) Algorithm

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Abstract

In this research, the primary objective is to ensure the appropriate functioning of transmission components, particularly the gearbox, which is highly prone to wear due to carrying the load directly. Condition monitoring and predictive maintenance of the gearbox are essential to prevent failures that can result in downtime and costly repairs. To simulate the wear in a controlled manner, tooth breakage and pitting were artificially induced using EDM. The raw vibration data obtained from an accelerometer sensor were then imported to LabVIEW software via a data acquisition system and analyzed in time and frequency domains at varying speeds and loads. The time-domain analysis included metrics such as “RMS and kurtosis,” while the frequency-domain analysis involved features such as "order spectrum." Additionally, time–frequency domains, such as "DWT and CWT," were utilized to provide a more comprehensive analysis of the gearbox's health. To classify the results obtained, support vector machining was used. The results obtained from the analysis provide a more in-depth understanding of the predominant types of wear in gearboxes and can be used to develop effective condition monitoring and predictive maintenance strategies to improve the reliability and lifespan of transmission systems.

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Abbreviations

CM-PdM:

Condition monitoring and predictive maintenance

EDM:

Electrical discharge machining

RMS:

Root mean square

DWT:

Discrete wavelet transform

CWT:

Continuous wavelet transform

TB1:

Tooth breakage 25%

TB2:

Tooth breakage 50%

TB3:

Tooth breakage 100%

PT1:

Pitting 1 mm

PT2:

Pitting 2 mm

PT3:

Pitting 3 mm

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Acknowledgments

We gratefully acknowledge the financial support and guidance provided by the TIH Foundation for IoT & IoE, IIT Bombay [Grant No.: TIH-IoT/2022-07/HRD/CHANAKYA/SL/CGP-A-04_010.]. We would like to express our sincere appreciation to The National Institute of Engineering, Mysuru, for their collaboration and valuable input throughout the research process. We also extend our gratitude to Mr. Shekhar Singh for his valuable assistance and support during various stages of this research. This work would not have been possible without the contributions of these institutions and individuals, and we remain indebted to them for their invaluable support."

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Authors and Affiliations

  1. Department of Mechanical Engineering, The National Institute of Engineering (South Campus), Mysuru, India

    I. M. Jamadar, R. Nithin, S. Nagashree, V. R. Prajwal Prasad, M. Preetham & P. K. Samal

  2. L&T Defence, Mumbai, India

    Shekhar Singh

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Jamadar, I.M., Nithin, R., Nagashree, S. et al. Spur Gear Fault Detection Using Design of Experiments and Support Vector Machine (SVM) Algorithm. J Fail. Anal. and Preven. 23, 2014–2028 (2023). https://doi.org/10.1007/s11668-023-01742-4

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