Prognostic is a rapidly developing field and seeks to build on current diagnostic equipment capabilities for predicting the system state in advance. In machine condition prognostics, the current and past observations are used to predict the upcoming states of the machine. Signal-de-noising and extraction of the weak acoustic signature are crucial to gearbox prognostics since the inherent deficiency of the measuring mechanism often introduces a great amount of noise to the signal. In addition, the signature of a defective gearbox is spread across a wide frequency band and hence can easily become masked by noise and low frequency effects. As a result, robust concepts are needed to provide more evident information for gearbox performance assessment and prognostics. This paper introduces enhanced and robust prognostic concepts for gear tooth based on an optimal wavelet filter method for fault identification and a statistical method for performance degradation assessment. The experimental results demonstrate that the gear tooth defect can be detected and evaluated at an early stage of development when both the optimal wavelet filter and statistical analysis technique are used.
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