The paper presents health indices (HI) for monitoring and prognostics of machine condition. HI are developed using morphology and entropy based complexity measures of machine vibration signals. The indices are compared with a recently introduced energy based feature and the commonly used statistical measure of signal kurtosis. The procedure of extracting HI is illustrated first using the simulated response of a simple gear model with tooth crack. Next the HI extraction process is applied to the experimental vibration data of a helicopter drivetrain gearbox with a seeded tooth fault.

Bearing envelope analysis (BEA) is a powerful technique in the detection of faults in bearings. The improper selection of the envelope window frequency and window bandwidth can render the analysis ineffective. This can reduce the ability of a health and usage monitoring system (HUMS) to correctly identify a degraded bearing. This occurred recently: a teardown analysis (TDA) of a utility helicopter oil cooler fan housing found extensive bearing damage. The HUMS did not detect the fault. This paper is an analysis of why the BEA failed to detect the damage bearing.