Aircraft Landing Diagnosis Using Fault Trees and Root Cause Analysis
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Solution Overview
Problem
Current aircraft landing performance analysis relies heavily on pilot observations, which are prone to human errors and subjective interpretations, and is typically conducted only after major incidents or deviations from regulatory norms, failing to provide consistent and timely feedback for optimizing landing performance.
Innovation Solution
An automated system that receives aircraft landing performance parameters across various phases, identifies deviations, develops a fault tree, performs root cause analysis using a high-level reasoning model, and displays the root cause, along with recommended maintenance actions, to optimize landing performance without relying on reported incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pilot observations are used for landing performance analysis, then the analysis can be conducted with simple tools, but the results are prone to human errors and subjective interpretations
Solution Approach 1:
The patent replaces the manual mechanical process of pilot observation and assessment with an automated computer-based system that processes flight data electronically. This substitution eliminates human subjectivity while maintaining ease of implementation through software-based analysis of existing flight parameters.
Solution Approach 2:
The system creates an automated digital copy of the landing performance assessment process, replicating and analyzing flight data objectively without requiring human interpretation. This digital copying enables consistent, repeatable analysis that eliminates human error while preserving the essential assessment functions.
2Device complexity
If analysis is conducted only after major incidents or deviations from regulatory norms, then the analysis process remains simple, but timely feedback for optimizing landing performance is not provided
Solution Approach 1:
The system performs preliminary automated analysis of landing performance continuously, rather than waiting for incidents to occur. By proactively analyzing flight data in real-time or near-real-time, the system provides timely feedback that enables preventive optimization before problems escalate to incident level.
Solution Approach 2:
The patent implements an automated feedback mechanism that continuously monitors landing performance and provides timely information back to operators and maintenance systems. This feedback loop enables rapid identification and correction of performance deviations without waiting for regulatory threshold breaches.
3Reliability
If automated analysis is implemented across all landing phases, then consistent and timely feedback is provided, but the system complexity increases
Solution Approach 1:
The patent divides the landing process into distinct phases (approach, flare, touchdown, ground roll) and applies automated analysis to each segment. This segmentation allows the complex analysis to be broken down into manageable components while maintaining overall consistency and reliability across the entire landing sequence.
Solution Approach 2:
The automated analysis system is designed to handle multiple landing phases and various performance parameters within a single unified platform. This multi-functional approach consolidates what could be multiple separate systems into one versatile tool, reducing overall system complexity while maintaining comprehensive coverage.
Data Source
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AI summary
A method for an automated aircraft landing analysis including: receiving (202) one or more aircraft landing performance parameters for one or more landing phases; determining (204) a landing performance deviation for each of the one or more landing phases in response to the one or more aircraft landing performance parameters; identifying (206) at least one of a system fault, a failure, and a pilot error that could have led to the landing performance deviations for each of the one or more landing phases; developing (208) a fault tree for the landing performance deviations for each of the one or more landing phases; identifying (210) measurable parameters, calculable parameters, inferable parameters, or observable parameters within the fault tree; converting (212) the fault tree into a high level reasoning model using a standard inference methodology; performing (214) a root cause analysis; identifying (216) a root cause of the landing performance deviation; and displaying (218) the root cause of landing performance deviation.