CBM Program for US Army Aircraft

A former Rochester, NY consultant, Carl Byington emphasizes data and analytics-based approaches at PHM Design, LLC in Atlanta, GA. He works with clients to implement predictive analytics to maximize operational and maintenance efficiency. Well published in his field, Carl Byington presented the condition-based maintenance (CBM) of the United States Army aircraft systems at an American Helicopter Society (AHS) specialists’ meeting.

A CBM program involves moving from part replacements performed at defined intervals to maintenance performed upon “evidence of need". With the context of the Army’s CBM+ plan, this requires a move away from “time before overhaul” (TBO) protocol that traditionally defines schedules for military vehicle component maintenance. It also suggests an ability to move away from dedicated inspection and test flight maintenance events.

According to the paper, major obstacles in CBM adoption include the limited ability of digital source collectors (DSC) and health and usage monitoring systems (HUMS) to diagnose component faults early. Issues include condition indicators and sensors’ sensitivity to operating/environmental conditions, observability of specific failure modes, and inherent signal to noise ratio. Fleet-wide diagnostic thresholds to action are also difficult to implement, with uncertainties arising in detecting existing and progressive damage or wear among individual aircraft variations of use. Implementing prognostics on faulty or degrading components is inherently a significant endeavor, with validating and verifying such systems a remaining challenge.

The paper presents tools that can improve data monitoring, boost diagnostics, and enable prognostics to be implemented in ways that support a better transition to CBM. The realizable benefits are substantial. Detection of faults in their early stages provides an opportunity to order parts, schedule personnel, shutdown the equipment before serious damage occurs, and minimize the disruption to production and missions. Furthermore, insight gained from better diagnostics reduces uncertainty regarding the “health” of critical internal drivetrain components and allows for the safe reduction of some preventive maintenance and inspections. In other words, maintenance is performed only when necessary. As we extend such capability into predictive prognostics technology, often enabled now by machine learning (ML) and artificial intelligence (AI) techniques, we can realize even greater maintenance and logistics benefits.