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<p>The gearbox is a critical mechanical system composed mainly of gears and bearings that ensure smooth power transmission and operational efficiency. Unexpected failures in these components often lead to significant economic losses and unplanned machine downtime, making early fault detection a crucial area of research. During operation, faults such as gear tooth cracking, pitting, wear, or bearing defects can alter the vibration response of the system. These vibration variations serve as key indicators for diagnosing the health condition of individual components. Therefore, vibration-based condition monitoring has become a widely adopted and reliable approach for gearbox fault detection. This review comprehensively examines various fault types associated with gears and bearings and provides an in-depth discussion of dynamic modeling approaches, including lumped parameter modeling, finite element modeling, multi-body dynamics, and hybrid data-physics integrated models. It further highlights recent research developments, comparative analyses, and emerging trends in time-varying mesh stiffness estimation and coupled gear-bearing interaction modeling. The review aims to bridge the gap between conventional physical models and modern hybrid approaches, offering a consolidated understanding of gearbox dynamics and fault diagnosis techniques relevant to both academia and industry.</p>

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Detection of gearbox faults using dynamic modeling techniques: a review

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Gagandeep Sharma,
  • Tejbir Kaur,
  • Sanjay Kumar Mangal

摘要

The gearbox is a critical mechanical system composed mainly of gears and bearings that ensure smooth power transmission and operational efficiency. Unexpected failures in these components often lead to significant economic losses and unplanned machine downtime, making early fault detection a crucial area of research. During operation, faults such as gear tooth cracking, pitting, wear, or bearing defects can alter the vibration response of the system. These vibration variations serve as key indicators for diagnosing the health condition of individual components. Therefore, vibration-based condition monitoring has become a widely adopted and reliable approach for gearbox fault detection. This review comprehensively examines various fault types associated with gears and bearings and provides an in-depth discussion of dynamic modeling approaches, including lumped parameter modeling, finite element modeling, multi-body dynamics, and hybrid data-physics integrated models. It further highlights recent research developments, comparative analyses, and emerging trends in time-varying mesh stiffness estimation and coupled gear-bearing interaction modeling. The review aims to bridge the gap between conventional physical models and modern hybrid approaches, offering a consolidated understanding of gearbox dynamics and fault diagnosis techniques relevant to both academia and industry.