Fatigue Level Calculation for Aircraft Maintenance Optimization
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Solution Overview
Problem
Current maintenance systems for equipment, such as aircraft, rely on regular inspections and anomaly detection based on sensing data, but fail to predict equipment fatigue progression due to operation patterns, leading to unnecessary maintenance or delayed failures.
Innovation Solution
A fatigue level calculating device and method that acquires environment information and calculates the relationship between operation conditions and fatigue levels, using sensors and statistical or machine learning techniques to predict fatigue based on operation patterns, allowing for timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regular inspections are performed uniformly on all equipment, then maintenance safety is improved, but maintenance costs increase due to unnecessary early maintenance
Solution Approach 1:
The system performs preliminary actions by calculating fatigue levels based on operation patterns before actual deterioration occurs. This allows maintenance to be scheduled proactively based on predicted fatigue levels rather than reacting to anomalies after they occur, optimizing the timing of maintenance activities.
Solution Approach 2:
The system changes the parameter basis for maintenance scheduling from uniform time-based intervals to operation-pattern-based fatigue level calculations. By using operation patterns (flight hours, distance, altitude, temperature) as variables, the maintenance schedule adapts to actual equipment usage and environmental conditions, preventing both premature and delayed maintenance.
2Measurement precision
If anomaly detection based on sensing data is used, then equipment deterioration is detected, but future deterioration progression cannot be predicted
Solution Approach 1:
The system performs preliminary calculation of fatigue levels using operation patterns before actual deterioration occurs. By analyzing historical operation data and environmental conditions, the system predicts future fatigue accumulation, enabling proactive maintenance scheduling rather than reactive anomaly detection.
Solution Approach 2:
The system establishes a feedback loop where operation patterns are continuously monitored, fatigue levels are calculated and updated, and maintenance decisions are made based on this continuous information flow. This creates a dynamic system that adapts to changing operation conditions and provides ongoing predictions of equipment state.
3Productivity
If equipment is maintained based on operation patterns, then maintenance timing is optimized, but complexity of maintenance system increases
Solution Approach 1:
The system achieves universality by using a standardized fatigue level calculation framework that can be applied across different equipment types and operation patterns. The core methodology remains consistent while adapting to various inputs (flight hours, distance, altitude, temperature), reducing the need for equipment-specific complex systems.
Solution Approach 2:
The fatigue level calculation acts as an intermediary that translates complex operation patterns and environmental conditions into a single, actionable metric. This intermediary layer simplifies the decision-making process by converting multiple variables into a unified fatigue assessment that directly informs maintenance scheduling.
Data Source
AI summary
A fatigue level calculating device includes an environment information acquiring unit that acquires environment information pertaining to an environment in the surroundings of equipment, and a fatigue level calculating unit that calculates a relationship between an operation condition of the equipment and a fatigue level of the equipment based on the environment information.