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Purpose <p>Fault diagnosis in a multi-stage gearbox test rig subjected to fluctuating speeds by utilizing the estimated Hjorth’s parameters has been carried out.</p> Methods <p>The gearbox’s condition is assessed across nine health states, with raw acoustic and vibration signals being collected for analysis. Three distinct input datasets from the health indicators (Hjorth’s parameters) for acoustic and vibration signals are constructed and then undergo feature learning via traditional machine-learning methods.</p> Results <p>The efficiency of the combined dataset while accomplishing the defect diagnostics and achieving better classification accuracies (~ 100%) in distinguishing between multiple health conditions of the gearbox is investigated.</p> Conclusion <p>Thus, the ability to perform defect diagnostics and characterize the health scenarios of the multi-stage gearbox with negligible reliance on the post-processing approaches and human intervention is demonstrated, establishing the scope for intelligent defect diagnostics.</p>

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Combined Vibro-acoustic Analysis to Identify the Health State of a Multi-stage Gearbox Through Hjorth’s Parameters

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Vamsi Inturi,
  • Rishabh Talwar,
  • Sabareesh G. R.

摘要

Purpose

Fault diagnosis in a multi-stage gearbox test rig subjected to fluctuating speeds by utilizing the estimated Hjorth’s parameters has been carried out.

Methods

The gearbox’s condition is assessed across nine health states, with raw acoustic and vibration signals being collected for analysis. Three distinct input datasets from the health indicators (Hjorth’s parameters) for acoustic and vibration signals are constructed and then undergo feature learning via traditional machine-learning methods.

Results

The efficiency of the combined dataset while accomplishing the defect diagnostics and achieving better classification accuracies (~ 100%) in distinguishing between multiple health conditions of the gearbox is investigated.

Conclusion

Thus, the ability to perform defect diagnostics and characterize the health scenarios of the multi-stage gearbox with negligible reliance on the post-processing approaches and human intervention is demonstrated, establishing the scope for intelligent defect diagnostics.