There is no established threshold or limit for gear vibration based condition indicators (CI) that indicates when a gear is in need of maintenance. The best we can do is set CI thresholds statistically, based on some small probability of false alarm. Further, to the best of our knowledge, there is no single CI that is sensitive to every failure mode of a gear. This suggests that any condition based maintenance system for gears will have some form of sensor fusion.

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven helicopter OEMs to develop products using STG. However, the effect of multiple gears meshing simultaneously with the central gear and a large number of synchronous components (gears or bearing) in close proximity creates a problem on how to detect the gear fault location in a STG.

Operation and maintenance of offshore wind farms will be more difficult and expensive than equivalent onshore wind farms. Accessibility for routine servicing and maintenance will be a concern: there may be times when the offshore wind farm is inaccessible due to sea, wind and visibility conditions. Additionally, maintenance tasks are more expensive than onshore due to: distance of the offshore wind farm from shore, site exposure, and the need for specialized lifting equipment to install and change out major components.

When compared with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven the helicopter manufacturing community to develop products using the STG. However, this may pose a challenge for the current gear analysis methods used in Health and Usage Monitoring Systems (HUMS). Gear analysis uses time synchronous averages to separate in frequency gears that are physically close to a sensor.

Time Synchronous Average (TSA) is an essential algorithmic tool for determining the condition of rotating equipment. Given its significance to diagnostics, it is important to understand the algorithms performance characteristics. This paper addresses four topics in relation to the TSA performance characteristics. The first topic is the evaluation of the performance (measured against gear fault detection) of 6 different TSA algorithms.

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.