Engine Mission Profile Definition Using Field Data
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Solution Overview
Problem
Current methods for defining mission profiles for aircraft engines are limited in accuracy and do not effectively utilize real-world data from existing engines, leading to uncertainties in design and maintenance strategies.
Innovation Solution
A method and system that select deployed engines with similar components using a first similarity metric, collect field data on their usage and operating conditions, and create representative mission profiles using a second similarity metric to define mission profiles for new engines, leveraging a digital thread platform for data integration and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mission design methods are used based on customer requirements and domain expert knowledge, then the process is simple and quick, but the accuracy and reliability of mission profiles are limited
Solution Approach 1:
The system performs preliminary actions by selecting deployed engines and collecting their field data before creating mission profiles for the new engine. This advance preparation using real-world data from similar engines improves the accuracy of mission profiles while managing complexity through automated data collection and processing pipelines.
Solution Approach 2:
The system creates copies of mission profiles from selected deployed engines that have similar components to the new engine. By copying and adapting proven mission profiles from fielded engines rather than creating them from scratch using only expert knowledge, the system improves accuracy while the automation manages the complexity of analyzing multiple engine datasets.
2Reliability
If real-world field data from deployed engines is collected and analyzed, then the accuracy and reliability of mission profiles improve, but the complexity of data collection and processing increases
Solution Approach 1:
The system segments the complex task of mission profile creation into distinct modules: selecting deployed engines based on component similarity, collecting field data from those engines, creating representative mission profiles, and finally defining the new engine's mission profiles. This segmentation improves reliability through systematic data-driven processes while managing complexity by organizing the workflow into manageable, automated stages.
Solution Approach 2:
The system introduces a digital thread platform as an intermediary that automatically collects, stores, and processes field data from multiple deployed engines. This intermediary layer handles the complexity of data integration and management, allowing the mission profile creation process to leverage reliable real-world data without requiring manual data collection and processing complexity.
3Loss of information
If mission profiles are defined without using actual usage data from similar engines, then the design process is faster, but design uncertainties increase
Solution Approach 1:
The system incorporates feedback loops where field data from deployed engines continuously informs and refines the mission profiles for new engines. By automatically feeding real-world operational data back into the mission profile creation process, the system reduces design uncertainties through evidence-based adjustments while automation minimizes the time penalty of this iterative refinement.
Solution Approach 2:
The system performs preliminary data collection and analysis from deployed engines before finalizing mission profiles for the new engine. By preparing and analyzing field data in advance from similar engines, the system reduces design uncertainties early in the process while the automated pipeline ensures this preparation doesn't excessively extend the overall design timeline.
Data Source
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AI summary
Systems and methods (24, 30) for defining mission profiles for a new engine (10) are described. The method (30) comprises: selecting (32) deployed engines from a set of existing engines based on components of the new engine (10) using a first similarity metric; collecting (34) field data (402A, 402B,...402N) associated with the deployed engines, the field data (402A, 402B,...402N) comprising usage and operating conditions for the deployed engines; creating (36) representative mission profiles from the field data (402A, 402B,...402N) using a second similarity metric; and defining (38) the mission profiles for the new engine (10) using the representative mission profiles.