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The use of condition monitoring (CM) data to infer degradation state and remaining useful life (RUL) prediction has grown with increasing use of health monitoring systems. Most degradation modelling requires a detection threshold to be established and can only model a single dynamical behaviour for the degradation. Such approaches have limitations as detection thresholds can vary widely and a single model may not adequately describe a degradation path as it evolves. In this paper, the switching Kalman filter (SKF) is adopted. The SKF uses multiple dynamical models describing different degradation processes and the most probable model is inferred using Bayesian estimation. The advantages are that it does not depend on a fixed threshold for fault detection and can model the different degradation processes as they evolve. The SKF approach is applied to CM data from helicopter gearbox bearings and is shown as a promising tool for maintenance decision-making.

Most research work has been performed within laboratory environments with seeded defects or accelerated testing and there is little published in the public domain on in-service defects with operational helicopter HUMS data. This study focuses on the actual service experience gathered from HUMS equipped AH64D helicopter belonging to the Republic of Singapore Air Force. Operational HUMS data from three helicopters with similar in-service defects found on their Tail Rotor Gearbox are analyzed and correlated with tear-down inspection findings. From analysis of the HUMS vibration spectrum, fault patterns which show the progression from localised to generalised damage in the bearing can be observed consistently across the defective gearboxes. The observed fault patterns are extracted as Condition Indicators (CI) to diagnose the different stages of bearing degradation within the gearbox. As the defects between the gearboxes are similar, the historical trends of the extracted CIs are used to developed prognostic model using parameter estimation approach.

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