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<p>The ability to make helical gears operate efficiently along with reliability stands critical in industrial applications because gear failures produce prolonged system stoppages and expensive maintenance needs. This research combines vibration analysis techniques with modern machine learning approaches to identify helical gear failures before they trigger system breakdowns. Our experimental research utilizes Fast Fourier Transform (FFT) alongside accelerometers to detect unique vibration signatures which distinguish operational gears from defective gear systems. The implementation of Long Short-Term Memory (LSTM) networks alongside other machine learning models enhances detection accuracy which minimizes necessary maintenance procedures. This study leads to practical applications which enable predictive maintenance strategies to maximize operational performance while minimizing economic losses from gear breakdowns. The research paper adds value to existing knowledge through better fault detection approaches while demonstrating the economic advantages of industrial applied advanced technologies.</p> Graphical abstract <p></p>

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Advanced machine learning models for early detection of helical gear faults

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Sagar Ajanalkar,
  • Shailesh Birajdar,
  • Kanchan Rajput,
  • Ritesh Fegade,
  • Deepak Maslekar,
  • Appasaheb Raul

摘要

The ability to make helical gears operate efficiently along with reliability stands critical in industrial applications because gear failures produce prolonged system stoppages and expensive maintenance needs. This research combines vibration analysis techniques with modern machine learning approaches to identify helical gear failures before they trigger system breakdowns. Our experimental research utilizes Fast Fourier Transform (FFT) alongside accelerometers to detect unique vibration signatures which distinguish operational gears from defective gear systems. The implementation of Long Short-Term Memory (LSTM) networks alongside other machine learning models enhances detection accuracy which minimizes necessary maintenance procedures. This study leads to practical applications which enable predictive maintenance strategies to maximize operational performance while minimizing economic losses from gear breakdowns. The research paper adds value to existing knowledge through better fault detection approaches while demonstrating the economic advantages of industrial applied advanced technologies.

Graphical abstract