Flight Data Validation for False Positive Reduction
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Solution Overview
Problem
Flight data from recorders often contains poor quality data, leading to numerous false positive detections of events, wasting time in processing invalid events, as many detected events are caused by glitches in the recording system rather than actual aircraft issues.
Innovation Solution
Implementing a flight data validation process that cleans the input data by identifying and excluding invalid values and parameters before performing analysis, using tests such as repeating pattern detection, spike detection, range comparison, and correlation tests to ensure only reliable data is analyzed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If flight data analysis is performed directly on raw recorder data, then all detected events can be identified, but many false positive detections occur due to poor data quality
Solution Approach 1:
The patent applies preliminary action by performing data validation and cleaning operations before the main flight data analysis process. The system executes validation tests (range checks, correlation tests, spike detection) on raw recorder data to identify and flag invalid data points prior to event detection analysis, thereby preventing false positives from being generated in the first place
Solution Approach 2:
The patent introduces an intermediary validation layer between the raw data source and the analysis engine. This intermediary component performs quality assessment and data cleaning operations, acting as a mediator that filters out problematic data points before they reach the event detection algorithms, thus improving reliability without significantly increasing processing time
2Reliability
If comprehensive flight data validation is performed, then false positive detections are reduced, but data processing time increases
Solution Approach 1:
The patent segments the validation process into multiple independent test modules (range validation, correlation tests, spike detection, repeating pattern detection). Each module operates independently on specific aspects of data quality, allowing the system to apply only necessary validation tests to different data types and reducing overall processing overhead through modular operation
Solution Approach 2:
The patent implements partial validation action by applying validation tests selectively rather than uniformly to all data points. The system uses heuristics and data characteristics to determine which validation tests are necessary for each dataset, avoiding excessive validation on already-clean data and focusing computational resources where they are most needed
Data Source
AI summary
A computer-implemented flight data monitoring method is provided for monitoring flight data, for example flight data obtained by a flight data recorder or quick access recorder on an aircraft. The method comprises receiving input flight data, performing flight data validation on the input flight data to generate validated flight data, and performing flight data analysis on the validated flight data to detect at least one predetermined type of flight event. The events may be useful for generating an aircraft safety report, for example. By validating the data before performing the analysis, the number of events which are falsely detected based on invalid or unreliable data can be reduced.