Engine System Modeling for Harmonized Maintenance Intervals
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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
1Productivity
If logistics forecast model is performed late in the design process after MBSE is completed, then the design process can be completed efficiently, but maintenance intervals are suboptimal and components may be replaced earlier than needed
Solution Approach 1:
The logistics forecast model is integrated into the MBSE process and executed during the design phase rather than after completion. This preliminary action allows maintenance predictions to inform design decisions, enabling optimization of maintenance intervals before the design is finalized, thus avoiding premature component replacements.
Solution Approach 2:
A feedback loop is established where the logistics forecast model continuously provides maintenance predictions back to the MBSE system. This feedback enables iterative refinement of the design to harmonize maintenance intervals across components, allowing the system to learn from predictions and adjust design parameters accordingly.
2Adaptability or versatility
If different components have varying life cycles, then each component can be optimized for its specific function, but unnecessary maintenance operations and increased costs occur due to non-harmonized intervals
Solution Approach 1:
The system modifies design parameters of components to harmonize their maintenance intervals. By adjusting parameters such as material selection, design life, or operational constraints, the system aligns the life cycles of different components, ensuring they require maintenance at similar intervals, thus reducing unnecessary maintenance operations and costs.
3Reliability
If maintenance intervals are not harmonized across components, then individual component performance can be maximized, but overall system efficiency decreases due to premature replacements and multiple maintenance operations
Solution Approach 1:
The MBSE system is enhanced with multi-functionality to simultaneously optimize individual component performance and harmonize maintenance intervals across the entire system. The integrated logistics forecast model serves multiple purposes: predicting maintenance needs for individual components while also identifying opportunities to synchronize maintenance schedules, thereby improving overall system efficiency without sacrificing component reliability.
Data Source
AI summary
A method for designing a system implementing model-based system engineering with maintenance forecasting may utilize a systems model and a maintenance forecast model. The systems model receives an initial systems design, generates an initial set of system design specifications based on the initial systems design, generates an initial concept of operations based on the initial systems design, and outputs the initial set of system design specifications and the initial concept of operations to the maintenance forecast model. The maintenance forecast model determines 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. The systems model then receives the first maintenance prediction from the maintenance forecast model, and creates a first updated systems design based on the first maintenance prediction.