Aviation Risk Detection via Machine Learning SWIM Analysis

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Solution Overview

Problem

Current methods for identifying risks in aviation operations rely on simple statistics and struggle to detect nonobvious patterns, often missing precursors to potential risks, which can lead to safety issues due to the inability to identify corrective actions.

Innovation Solution

A system using machine learning techniques integrated with System Wide Information Management (SWIM) data to detect risk precursors, providing a visual interface for real-time analysis and prediction of potential safety issues in airport operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If simple statistical methods are used to identify risks, then the analysis is easy to perform, but the ability to detect nonobvious patterns and risk precursors is lost

Engineering Contradiction:

Improveease of risk analysisVSAvoidloss of risk precursor information

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The patent introduces an intermediary processing layer between raw flight data and risk identification. This layer includes data normalization, feature extraction, and pattern recognition algorithms that transform raw data into meaningful risk indicators while preserving subtle precursor patterns that simple statistics would miss.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual statistical analysis with automated machine learning algorithms and neural networks. These systems automatically detect complex patterns, correlations, and precursors in flight data without requiring human analysts to manually review simple statistics, thereby preserving information that would otherwise be lost.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If detailed analysis of individual events is performed to identify root causes, then risk understanding may improve, but the process is time-consuming and many events are dismissed as outliers

Engineering Contradiction:

Improveprecision of risk identificationVSAvoidtime for risk analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary processing of flight data by pre-computing features, normalizing data formats, and establishing baseline patterns before actual risk analysis is needed. This preliminary action prepares the data in advance, enabling rapid detailed analysis when risks are detected without repeating the entire analysis pipeline.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a two-tiered analysis approach: first applying broad pattern recognition to identify potential risk areas, then performing detailed analysis only on those specific cases. This partial action approach avoids the time cost of analyzing every individual event while maintaining precision for the most critical cases.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If machine learning techniques are used to identify risk precursors, then detection accuracy improves, but system complexity increases

Engineering Contradiction:

Improveprecision of risk precursor detectionVSAvoidcomplexity of analysis system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent divides the complex machine learning system into modular components: data collection modules, preprocessing modules, feature extraction modules, pattern recognition modules, and visualization modules. Each module performs a specific function and can be independently developed, tested, and maintained, reducing overall system complexity despite the advanced capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent designs universal data structures and processing pipelines that can handle multiple types of flight data (flight parameters, weather data, air traffic control communications, maintenance records) through a single unified machine learning framework. This multi-functionality reduces complexity by avoiding separate specialized systems for each data type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20240330819A1System and methods for risk and risk precursor identification in commercial aviation operations

Publication Date: 2024.10.03 UNITED STATES OF AMERICA AS REPRESENTED BY THE ADMINISTRATOR NAT AERONAUTICS & SPACE ADMINISTRATION

AI summary

System and methods for risk and risk precursor identification in commercial aviation operations according to various aspects of the present invention operate in conjunction with a source of operation flight track data, a set of risk determination models containing instructions on how to process a set of received operational flight track data, a risk determination API for activating one or more risk models to process the set of received operational flight track data, and a user interface for communicating with the risk determination API. Each risk model may be trained to analyze flight track data to identify a particular type of risk precursor or identify a type of risk and any precursors that led to the risk. Processed results and identified risk precursors are forwarded to the user interface and displayed to allow users to quickly distinguish between nominal conditions for an aircraft and conditions with elevated levels of risk.