Space Weather Root Cause Analysis for Spacecraft Systems
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Solution Overview
Problem
Spacecraft failures due to space weather events are challenging to diagnose due to the complexity of high-dimensional sensor data and the interplay between space weather conditions and spacecraft systems, making it difficult to determine the root cause of failures using pre-defined rules.
Innovation Solution
A system comprising a hardware processor and memory with executable program code that includes a classifier to identify space weather events causing failures and a task manager to determine the affected spacecraft system, performing corrective actions to prevent future failures, using attribute selection, classification models, and data analysis to distinguish between normal and abnormal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-defined rules are used for analysis, then the analysis process is simple and fast, but the analysis is not adequate to address the complexity of space weather and high-dimensional sensor data
Solution Approach 1:
The patent replaces traditional mechanical rule-based analysis systems with machine learning models that can automatically learn complex patterns from high-dimensional sensor data. The classifier uses trained models to identify space weather events and their relationship to spacecraft failures, substituting manual rule-definition with automated intelligent analysis that handles the complexity and interdependencies in the data.
2Measurement precision
If many sensors are used to collect data, then the data coverage and detection capability are improved, but the data dimensionality and analysis complexity increase
Solution Approach 1:
The patent extracts and identifies only the most relevant features and patterns from the high-dimensional sensor data using machine learning techniques. The classifier focuses on extracting meaningful signals related to space weather events and spacecraft system responses, filtering out redundant information and reducing data dimensionality while maintaining detection precision.
Solution Approach 2:
The patent transforms the high-dimensional raw sensor data into meaningful features and parameters through machine learning processing. By changing the representation of the data from raw high-dimensional values to extracted features and patterns, the system reduces complexity while preserving the essential information needed for accurate analysis.
3Measurement precision
If the relationship between space weather type and spacecraft condition is analyzed in detail, then the root cause identification accuracy is improved, but the analysis complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary analysis by training machine learning models on historical data before actual space weather events occur. The classifier is pre-trained to recognize patterns and relationships between space weather types and spacecraft conditions, so that when real events occur, the system can quickly identify root causes using the pre-established knowledge rather than performing complex real-time analysis.
Solution Approach 2:
The patent creates a computational model that copies and simulates the complex relationships between space weather and spacecraft systems. By training the machine learning classifier on historical data, the system creates a virtual representation of these relationships that can be queried efficiently without requiring complex real-time analysis of the actual physical systems.
Data Source
AI summary
Methods and systems for preventing spacecraft damage include identifying a space weather event that corresponds to a spacecraft system failure. A spacecraft system is determined that causes the spacecraft system failure, triggered by the space weather event. A corrective action is performed on the determined spacecraft system to prevent spacecraft system failures from being triggered by future space weather events.