The one or more processors can execute the instructions and cause the one or more processors to perform operations that include obtaining historical data indicative of operational conditions of the gas turbine engine. The one or more memory devices store instructions and data. The system includes one or more memory devices and one or more processors. In response to determining that the flight data is indicative of a usable flight, the method further includes constructing a model, based at least in part on historical data, correlating low cycle fatigue consumption with the flight data using a machine learning technique.Īnother example aspect of the present disclosure is directed to a system for modeling usage based life consumption of a gas turbine engine. The method further includes determining, by one or more computing devices, whether the flight data is indicative of a usable flight. The method further includes obtaining, by one or more computing devices, flight data indicative of one or more operational conditions of an aerial vehicle during an operating period. The method includes obtaining, by one or more computing devices, historical flight data indicative of one or more operational conditions of an aerial vehicle. One example aspect of the present disclosure is directed to a method for constructing a model correlating low cycle fatigue consumption with flight data. BRIEF DESCRIPTIONĪspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or may be learned from the description, or may be learned through practice of the embodiments. Analogous to the design phase lifespan designation, these existing methods may not be ideal for near real-time or real-time application and do not include characteristics to account for actual operating conditions, such as flight data or engine performance, which can be considered and used to extend availability, reliability, and safety of operation of GTEs. The physics-based approach can be computationally intensive and involves relying on a representative set of operating periods or field missions to evaluate fatigue life limits of machinery components. Some existing methods for determining fatigue life consumption adopt a physics-based approach that is comparable to the design phase lifespan designation. This limitation can result in conservative life span estimates and subsequent change of remaining engine life span or engine damage due to different operation or operational conditions that were not considered during design. The limitation of the design phase calculations and life span designations are that they cannot take into account actual operating conditions. This lifespan designation is often done during the design process and involves detailed calculations of stresses and temperatures for a standard flight, and the use of material property and fatigue models. In engine structural life computations, it is common to designate a lifespan for certain components based on a certain number of start-stop cycles and based on a standard flight or missions. Material fatigue can result in damage to and eventually the failure of machinery components. Material fatigue can impact the lifespan, availability, reliability and safety of operation of machinery, such as gas turbine engines (GTE). Material fatigue is a common phenomenon where structures fail when subjected to a cyclic load. AETP is a technology maturation and risk reduction program leading to testing of two XA100 flight-weight large combat engines.The present subject matter relates generally to digital systems for predicting cyclic life consumption of machinery components, such as components of gas turbine engines. Job Description SummaryThe XA100 Digital Sustainment Programs Leader for the Edison Works Adaptive Engine Transition Program (AETP) will lead the initiative to develop and implement a digital solutions framework that will enable logistics data development and military production engine sustainment requirements.

Usage based lifing