Browse Topic: Prognostics
ABSTRACT
The U.S. Department of Defense has begun the acquisition of the next generation of military rotorcraft, named Future Vertical Lift (FVL), to replace its aging fleet. U.S. Army Futures Command intends to sustain FVL under a new strategy of maintenance free operating periods (MFOP). This study developed a discrete event simulation to evaluate MFOP success given component reliabilities, desired MFOP duration, and operational tempo of a battalion with thirty aircraft. The simulation compared notional FVL aircraft with improved reliability to today's fleet. Results indicated that inherent reliability alone was insufficient to achieve MFOP goals and that prognostics and diagnostics with robust information management are necessary. Sensitivity studies found the recovery effort after an MFOP was linked to the MFOP duration. Recovery downtime was tied to both the duration and operational tempo. Availability and cost improved with moderate gains in MFOP duration by eliminating unnecessary
Over the last decade or more there has been a concerted push to move from on condition to predictive maintenance to improve rotorcraft availability and cost competitiveness of sustainment (Ref. 1-2). The US Army, along with industry partners, have been working on the development of prognostics for complete rotorcraft coverage. It has been identified that accurately capturing maintenance actions is needed to improve the accuracy of prognostics for better component health state awareness. Further to achieve the Army's vision for Zero Maintenance rotorcraft and meet the Maintenance Free Operating Period (MFOP) (Ref. 3) requirements for the Future Vertical Lift (FVL) program, it's essential to have an automated configuration management system. To help meet these objectives, the Army and Honeywell are working on the Rotorcraft Automated Component Tracking (RACT) Science and Technology (S&T) development program. This paper discusses the research being conducted to enable the Army's RACT
This paper summarizes the development and implementation of material-based prognostic technology for modeling fatigue damage in rotorcraft drive system rotating components considering manufacturing process, contact pattern, lubrication effects, stresses, microstructure, and material variability. The AMS 6265 and AMS 6308 prognostic models were developed and applied to predict contact and bending fatigue damage in the planetary gear system. Prognostic model was verified by comparing fatigue life simulation results with industry test data. Prognostic model results were demonstrated and designed for integration with onboard and offboard elements of industry health monitoring/management systems. This paper includes details on predicting contact fatigue and bending fatigue life, endurance limits, maximum continuous power (MCP) rating, and overload effects. Prognostic model results show the application of this gear health algorithm solution in rotorcraft drive system transmission gear design
Health and Usage Monitoring Systems (HUMS) generate a significant amount of data used for on-board and off-board monitoring of the health of the aircraft and its components. When this data is aggregated over the life of an aircraft, it becomes an invaluable resource that enables decision making for diagnostics, prognostics, and fleet management. At the fleet level, the amount of data being ingested, stored, and processed becomes a challenge in itself. The capability to easily handle data of this size is critical to be responsive to time-critical inquiries, iterate on data modeling, and enable efficient diagnostics and prognostics algorithm development. This paper discusses how massively scalable data analytics technologies have been used to enable rapid decision support using HUMS and other data sources. Several use cases are highlighted to show the novel opportunities enabled by these technologies along with associated challenges.
ABSTRACT The ability to prognosticate the future state of a mechanical component can greatly improve the ability of a helicopter operator to manage their assets. Fundamentally, prognostics can change the logistics support of a helicopter by: reducing spares, improving the likelihood of a deployment meeting its mission requirements, and reducing unscheduled maintenance events. A successful prognosis is based on applying a fault model and usage metrics (torque) to a diagnostic. This paper addresses a generalized fault and usage model through simplification of Paris' Law and the use of a Kalman Smoother. This state observer technique is a backward/forward filtering technique that has no phase delay. This allows a generalized, zero tuning model that provides an improved component health trend, and a better estimate of the current remaining useful life (RUL).
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