An End-to-End PHM Framework for Industrial Compressor Gearbox Fault Detection and RUL Prediction
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Abstract
Gearbox health monitoring in industrial machinery is essential for maintaining production efficiency, reducing maintenance costs, and ensuring reliable operation. However, the validation of complete prognostic frameworks on real-world industrial gearboxes remains limited compared to studies conducted on controlled laboratory test rigs. This study presents a full end-to-end Prognostics and Health Management (PHM) framework validated on a run-to-failure dataset collected from an operational industrial spur gearbox used in compressor machinery. Vibration data is acquired using a radially mounted accelerometer while shaft speed is derived from a 1/rev tachometer signal. From the processed signals, condition indicators are extracted across multiple vibration analysis domains, including residual signal analysis, amplitude and frequency demodulation, energy operator techniques, and sideband modulation analysis. From these algorithms, features sensitivity to gear fault modes such as tooth cracking, pitting, scuffing, and misalignment are calculated. These condition indicators are fused into a single scalar health index which can determine when maintenance should be performed. A Remaining Useful Life (RUL) is estimated using a high cycle fatigue model, allowing stable real-time prediction without requiring detailed material-specific parameters. The RUL provides the operator with actional information as to when best to schedule maintenance. Results demonstrate that the proposed framework enables early fault detection and provides consistent, physically interpretable RUL predictions, supporting condition-based maintenance decisions and improving operational planning in industrial gearbox systems.
How to Cite
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Industrial Gearbox, RUL, Health Index, VIbrations, PHM
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