Multi-Stage Air Data Probe Prognostics for Real-Time RUL Prediction
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Solution Overview
Problem
Existing aircraft-based health monitoring systems lack the sophistication to analyze air data probe data in real-time for accurate prediction of remaining useful life and failure, requiring data transmission to a ground station and manual module updates.
Innovation Solution
A modular prognostics health monitoring system that includes edge devices for initial data processing, a smart coordinator for further analysis, and cloud infrastructure for detailed prediction of remaining useful life and failure using machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is transmitted to a ground station for analysis, then complex health monitoring algorithms can be executed, but real-time prediction capability is reduced and operational time is lost
Solution Approach 1:
The health monitoring system is segmented into multiple levels: edge device for real-time local analysis, coordinator for data aggregation, and cloud infrastructure for comprehensive processing. This segmentation enables simultaneous real-time prediction at the edge and detailed ground station analysis, resolving the contradiction between real-time capability and prediction accuracy.
Solution Approach 2:
The edge device performs preliminary data processing and initial health assessments locally before transmitting processed data to the ground station. This preliminary action reduces the time required for ground-based analysis while maintaining prediction accuracy through pre-filtered and pre-processed data.
2Adaptability or versatility
If the data acquisition module is updated, then monitoring capabilities are improved, but the module must be removed and reinstalled causing operational disruption
Solution Approach 1:
The data acquisition module is extracted as a separate, removable component with standardized interfaces. This allows the module to be updated independently without affecting the core probe structure, enabling capability improvements while minimizing operational disruption through hot-swappable design.
Solution Approach 2:
The data acquisition module is designed with universal, standardized connection interfaces that allow different versions of the module to work with the same probe structure. This multi-functionality enables seamless updates and upgrades without requiring custom installation procedures, improving both adaptability and ease of operation.
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 real-time, accurate prediction of air data probe failure and remaining useful life, reducing operational disruptions by allowing timely replacement of faulty probes and avoiding unnecessary replacements.
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 method for monitoring a vehicle-borne probe includes receiving, by a first edge device in communication with the probe, sensed data related to a characteristic of a heating element of the probe, analyzing, by a first application of the first edge device, the sensed data to generate a first data output, receiving, by a coordinator in communication with the first edge device, the first data output, and incorporating the first data output into a data package, receiving, by a cloud infrastructure in communication with the coordinator, the data package via a data gateway, and analyzing, by one of the cloud infrastructure and a ground station, the data package to estimate a remaining useful life and a failure of the probe.