Development of a vibration monitoring strategy based on cyclostationary analysis for the predictive maintenance of helicopter gearbox bearings

The scope of this paper is the development of a fault detection and diagnosis method aimed to helicopter gearbox bearings vibration monitoring in an operational context. Bearings are critical components in the gearbox, and their monitoring allows for failure anticipation capabilities, leading to increased safety and improved maintenance planning. Deploying a monitoring strategy for helicopter gearboxes necessitates the development of a methodology which can provide reliable information under varying operating conditions, dealing with a noisy vibration environment and simultaneously considering acquisition system constraints, such as limited acquisition duration and sampling frequency, and operational needs, such as low rate of false alarms and minimal workload for the analyst. The approach proposed in this paper is based on the cyclostationary signals theory and relies on a two-steps procedure of detection and diagnosis. First, bearing fault detection indicators are devised on a statistical basis, leveraging on the theoretical properties of the envelope method. Then, a diagnosis based on the computation of the averaged cyclic periodogram is performed to assess the damage in the eventuality of an alarm. The developed methodology is validated on real helicopter data collected over about twenty thousand f light hours, including four bearings from different machines for which in-service spalling initiation occurred. The fault detection performance is evaluated on the basis of the achieved false alarm rates and the improvement in fault anticipation with respect to chip detectors, whereas the capability of isolating the fault-related signals using cyclostationary signal separation methods is shown for the diagnosis stage.