Flight Data Streaming Analytics for Real-Time Aircraft Failure Detection
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Solution Overview
Problem
Existing vehicle health management systems face challenges in efficiently processing large amounts of real-time data from various sensors across multiple aircraft, leading to delays in providing timely and accurate health status information, which can result in increased downtime and maintenance costs.
Innovation Solution
A real-time streaming analytics method and system for flight data processing that receives and processes data streams from aircraft, selects relevant data based on flight modes, identifies sequences of events, determines statistical variables, compares them to historical models, and transmits health monitoring information to detect potential failures, utilizing a computing device with modules for data conversion, model building, and streaming analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time processing of large amounts of sensor data from multiple aircraft is implemented, then health status information timeliness is improved, but system complexity and processing resource requirements increase
Solution Approach 1:
The patent segments the complex data processing task into multiple streaming analytics operations that process different aspects of flight data independently. The system divides sensor data into separate streams for different parameters (engine performance, flight conditions, system status) and processes each stream through specialized analytics functions, reducing overall system complexity while maintaining real-time processing capability.
Solution Approach 2:
The patent applies preliminary action by pre-defining analytics operations and thresholds before data processing begins. The system establishes streaming analytics rules, statistical thresholds, and alert conditions in advance, allowing the data processing system to automatically evaluate incoming sensor data against pre-configured criteria without requiring complex real-time decision logic, thus reducing processing complexity.
2Measurement precision
If comprehensive sensor data from all aircraft systems is collected and processed, then measurement precision and health assessment accuracy are improved, but data processing time and computational load increase
Solution Approach 1:
The patent extracts only the most relevant features and statistics from comprehensive sensor data streams. Instead of processing all raw sensor data in detail, the system extracts key statistical measures (means, standard deviations, trends) and critical event sequences from each data stream, maintaining high health assessment accuracy while significantly reducing computational load and processing time.
Solution Approach 2:
The patent transforms raw sensor data into different parameter representations that are more efficient for analysis. The system converts time-series sensor readings into statistical parameters (averages, deviations, rates of change) and event sequences, changing the data parameters from raw measurements to processed indicators that maintain diagnostic accuracy while reducing processing complexity.
3Reliability
If statistical analysis and historical data comparison are performed in real-time, then failure detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing historical flight data models and statistical baselines before real-time processing. The system prepares reference datasets, normal flight patterns, and failure signatures in advance, allowing real-time comparison operations to simply match incoming data against pre-established criteria rather than performing complex historical analyses during critical processing windows.
Solution Approach 2:
The patent implements skipping by using incremental statistical updates rather than re-computing full statistical analyses for each new data point. The system maintains running statistics (cumulative means, variances) that can be updated efficiently with each new measurement, allowing rapid failure detection through simple comparisons rather than time-consuming recalculation of historical distributions.
Data Source
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AI summary
An example method includes receiving a plurality of data streams acquired for a respective parameter of a plurality of parameters indicating an operating condition of the aircraft, selecting at least one data stream corresponding to at least one parameter of the respective plurality of parameters, selecting a portion of data from the at least one data stream, comparing the portion of data to a model determined for the at least one parameter based on historical data, determining that a failure has occurred or is likely to occur during operation of the aircraft based on the comparing, and transmitting aircraft health monitoring information indicative of occurrence or likelihood of occurrence of the failure.