gearboxes are commonly used in rotating machinery for power transmission. a gearbox consists of shafts, gears, and bearings, each component having specific mechanical dynamics and fault properties. reliable gearbox fault detection and health monitoring techniques are critically needed in industries for more efficient predictive maintenance applications. the objective of this work is to develop a new technology for health monitoring of gearboxes. firstly, a new wavelet analysis method is technique for analysis of gear faults in a gearbox with demodulation from other rotating components such as shaft and bearings. secondly, a mode decomposition technique is proposed to highlight bearing fault features in a gearbox. thirdly, a new evolving neuro-fuzzy (enf) classifier is developed to integrate the merits of different fault detection techniques for real-time health condition monitoring of gear systems. the effectiveness of the proposed techniques is verified by simulation and experimental tests.