Engine System Modeling with Integrated Maintenance Forecasting
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Solution Overview
Problem
Current aircraft engine design processes lead to suboptimal maintenance intervals due to late logistics forecasting, resulting in increased costs and inefficiencies, as different components have varying life cycles, leading to premature replacements and unnecessary maintenance operations.
Innovation Solution
Implementing a model-based system engineering method that integrates maintenance forecasting, where a systems model receives initial design specifications, generates predictions, and updates the design to harmonize maintenance intervals and life cycles of components, using a maintenance forecast model to optimize maintenance schedules and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If logistics forecast model is performed late in the design process (after MBSE is completed), then the design process can be completed following standard procedures, but maintenance intervals become suboptimal leading to increased maintenance costs and premature replacements
Solution Approach 1:
The logistics forecast model is executed during the MBSE phase rather than after completion, performing the maintenance analysis action in advance. This allows maintenance intervals to be optimized before final design specifications are locked in, preventing suboptimal scheduling while maintaining standard design procedures.
Solution Approach 2:
The system implements iterative feedback loops where maintenance predictions from the logistics forecast model are fed back into the MBSE process. This allows design specifications to be refined based on maintenance optimization insights, and updated predictions are generated to verify improvements, creating a closed-loop optimization system.
2Reliability
If different components have varying life cycles (e.g., 2000 cycles vs 2500 cycles), then each component can be designed for its optimal performance, but premature replacements and unnecessary maintenance operations occur
Solution Approach 1:
The system modifies design parameters of components with longer life cycles to reduce their longevity, bringing them into alignment with shorter-lived components. For example, a component designed for 2500 cycles might have its parameters adjusted to achieve 2000 cycles, ensuring all components reach end-of-life simultaneously and eliminating premature replacements.
Solution Approach 2:
The logistics forecast model serves multiple functions: it predicts maintenance needs, identifies life cycle mismatches, suggests design modifications, and validates optimization results. This multi-functional approach allows a single system to address both component reliability and maintenance efficiency simultaneously.
3Ease of manufacture
If maintenance intervals are not optimized, then design and manufacturing can proceed without complex coordination, but increased costs for maintenance and parts result
Solution Approach 1:
Maintenance cost optimization is performed during the design phase rather than during operations. By calculating and optimizing maintenance intervals before the engine is built, the system prevents excessive maintenance spending without requiring complex coordination during actual maintenance operations.
Solution Approach 2:
The system creates a digital model (copy) of the engine system that includes all components and their life cycle characteristics. This virtual model allows maintenance optimization calculations to be performed on the copy without affecting the physical system, enabling cost analysis and optimization before manufacturing.
Data Source
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AI summary
A method for designing a system (100) implementing model-based system engineering with maintenance forecasting may utilize a systems model (102) and a maintenance forecast model (104). The systems model (102) receives (202) an initial systems design, generates (204) an initial set of system design specifications based on the initial systems design, generates (204) an initial concept of operations based on the initial systems design, and outputs (206) the initial set of system design specifications and the initial concept of operations to the maintenance forecast model (104). The maintenance forecast model (104) determines (208) a first maintenance prediction for the initial systems design based on the initial set of system design specifications and the initial concept of operations received from the systems model (102). The systems model (102) then receives (210) the first maintenance prediction from the maintenance forecast model (104), and creates (212) a first updated systems design based on the first maintenance prediction.