Probabilistic Method to predict Remaining Usage Life of Aircraft Structures



Mudit Rastogi


Current trends in the design of new aircraft components and high level innovations in the older type of aircraft are clearly pointing to automation and integration of all aircraft systems to give increased cost efficiency in the aircraft operation. The same trend can be observed in the design, operation and maintenance of aircraft structures where advanced Structure Health Monitoring (SHM) systems are about to enter into service. These advanced systems are designed to provide both diagnostic and prognostic information enabling application of Condition Based Maintenance (CBM) or even Prognostics Maintenance (PM) concepts into the maintenance of the aircraft structures. To make this CBM/PM concept a reality, an accurate, reliable and robust method to predict the Remaining Usage Life (RUL) of the structure is the foremost step.
This paper presents a probabilistic method for RUL prediction. A hybrid approach is used, comprising of two different algorithms. The first algorithm adopts fracture- mechanics based fatigue crack growth model. This approach uses physics of failure to predict the crack growth curve and underlying degradation process. It calculates the accurate value of Stress Intensity Factor (SIF) to calculate the crack growth curve. The second algorithm is a mathematical model which quantify various sources of uncertainty such as future loading, crack length, model parameters etc. The process described in this paper results in enhanced remaining usage life estimation by compensating for the aforementioned modeling uncertainties. The model results were verified and validated on a typical aero structure with experimental, FEA simulation and fractography data.

How to Cite

Rastogi, M. (2016). Probabilistic Method to predict Remaining Usage Life of Aircraft Structures. PHM Society European Conference, 3(1).
Abstract 329 | PDF Downloads 745



condition based maintenance (CBM), structural health monitoring, Prognostics Health Monitoring

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