Browse Topic: Fleet management
Traditional safe-life methodologies for rotorcraft structural components often result in overly conservative life estimates, increasing maintenance costs and reducing aircraft availability. This study explores the integration of digital twin concepts with probabilistic modeling and machine learning to enhance structural life assessment, demonstrated through a practical case involving the Royal Canadian Air Force CH-146 Griffon helicopter. A probabilistic fatigue model determines a fatigue life distribution by incorporating material variability and uncertain operational loads inferred directly from flight data. Unlike conventional approaches, this method dynamically estimates load spectra, including uncertainty instead of relying on conservative assumptions. Monte Carlo simulations are used to quantify structural risk and assess the impact of load and material uncertainties. Sensitivity analyses highlight these uncertainties’ contributions to failure probability. The proposed approach
Military rotorcraft engines operating in harsh environments routinely ingest large quantities of mineral dust, which can degrade components and ultimately reduce operability. Time off-wing for unscheduled maintenance is a costly burden, both financially and operationally. Rapidly predicting engine deterioration rates as a function of the mission presents an opportunity to optimise flow of supplies, better manage fleets, and perform safety risk assessments when dust loading is expected to be particularly high. In the current contribution, we present our ongoing efforts in this field with a new methodology for assessing the effectiveness of inertial particle separators and quantifying the changes they impart to the inbound dust. We demonstrate that both the concentration reduction and the modification to the particle size distribution can be made on the basis of a single independent variable- a generalised Stokes number for inertial particle separators- and a single performance parameter
ABSTRACT Airframes in the future will include a significant amount of composite material components that need to be designed for both optimal structural efficiency and damage tolerance. Current composite design methodology relies on the establishment of worst-case scenarios for each of the factors that influence the structural capacity and life of airframe components. The layered application of these factors can result in excessive levels of conservatism and maintenance requirements that reduce aircraft availability. The combat aircraft of the future can be designed and maintained based on specific knowledge derived from data driven methodologies to define risk, threat impact, and measured structural response in order to maximize aircraft availability, while ensuring safety and reliability. This work describes an Advanced Structural Integrity Framework (ASIF) that probabilistically models composite residual strength. Full-scale damage tolerance tests of a UH-60M stabilator provided
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.
Items per page:
50
1 – 50 of 69