Air Data Probe Health Monitoring With Edge-Cloud Failure Prediction
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Solution Overview
Problem
Existing aircraft-based health monitoring systems lack sophistication for real-time prediction of remaining useful life and predicted failure of air data probes, requiring data transmission to ground stations and manual module updates, and are prone to abrupt heating element failures due to prolonged usage and frequent switching.
Innovation Solution
A modular prognostics health monitoring system with edge devices and a smart coordinator that performs real-time data analysis using edge and cloud infrastructure, incorporating advanced algorithms for predicting imminent failures and estimating remaining useful life of air data probes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is transmitted to ground station for analysis, then sophisticated health monitoring algorithms can be applied, but real-time prediction capability is lost and operational time is wasted
Solution Approach 1:
The system segments health monitoring analysis into two parts: basic monitoring algorithms executed in real-time at the aircraft (edge computing), and sophisticated advanced algorithms executed later at the ground station. This segmentation enables both real-time prediction capability and sophisticated analysis to coexist, resolving the contradiction between immediate response and comprehensive analysis.
Solution Approach 2:
An edge computing platform is introduced as an intermediary between the probe sensors and the ground station. This intermediary performs preliminary data processing and basic health monitoring algorithms in real-time at the aircraft, enabling immediate predictions while preparing processed data for subsequent sophisticated analysis at the ground station.
2Measurement precision
If monitoring parameters are updated, then health monitoring accuracy is improved, but system complexity increases requiring module removal and reinstallation
Solution Approach 1:
The system employs dynamic parameter configuration where monitoring parameters can be updated remotely without physical module changes. The edge computing platform and ground station enable dynamic adjustment of monitoring thresholds, algorithms, and parameters through software updates, eliminating the need for module removal and reinstallation while maintaining improved health monitoring accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, real-time prediction of air data probe failures and timely replacement, minimizing operational disruptions by integrating edge and cloud analytics for sophisticated health monitoring without manual module updates.
Implementation Method 1
resistive heating elements are installed in the air data probes to prevent ice formation. To heat the probe, an operational voltage is provided through the heating element.
Data Source
AI summary
A coordinator for use in a system for monitoring a vehicle-borne probe includes a first communication interface configured to exchange data with at least one edge device of a plurality of edge devices, a second communication interface configured to exchanged data with a cloud infrastructure and at least one vehicle system, and a processing unit. The processing unit is configured to analyze synthesized data comprising first data outputs from at least one edge device of the plurality of edge devices, second data outputs from at least one edge device of the plurality of edge devices, and data from the at least one vehicle system. The processing unit is further configured to implement a data processing application to analyze the synthesized data to generate a third data output, and incorporate the synthesized data and the third data output into a data package.