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Micro-pitting is a fatigue effect that occurs in geared transmission systems due to high contact stress, and monitoring its progression is vital to prevent the eventual failure of the tooth flank. Parameter signature analysis has been successfully used to monitor bending fatigue failure and advanced phases of gear surface fatigue failure such as macro-pitting and scuffing. However, due to modern improvements in steel production the main cause of gear contact fatigue failure can be attributed to surface micro-pitting rather than sub-surface phenomena. Responding to the consequent demand to detect and monitor the progression of micro-pitting, this study experimentally evaluated the development of micro-pitting in spur gears using vibration and oil debris analysis. The paper presents the development of an online health monitoring system for use with back-to-back gear test rigs.

Gears are one of the most important parts of any mechanical transmission system, and in order to achieve reliable operation effective monitoring techniques must be employed. Predictive health monitoring (PHM) systems are currently gaining in popularity due to their effectiveness in providing robust information about the system condition and reducing maintenance costs. However, PHM systems require reliable monitoring techniques, such as vibration, acoustic emission, and oil debris analysis. These techniques have been studied in recent years to discover which can best support the operation of PHM systems in tracing the condition of the operating transmission. These studies have shown the need to apply intelligent algorithms in order to benefit from the advantage of each technique in classifying faults and predicting the onset of failure. This paper presents a new online PHM system for monitoring different gear faults using vibration analysis and autoregressive (AR) algorithms.