Automated Data Accountability for Missions in Mars Rover Data
| Autor: | Ryan Alimo, Brian Kahovec, Dylan Sam, Dounia Lakhmiri, Dariush Divsalar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
021103 operations research
010504 meteorology & atmospheric sciences business.industry Computer science Deep learning Real-time computing 0211 other engineering and technologies Process (computing) 02 engineering and technology Data loss 01 natural sciences Pipeline (software) Mars rover Computer data storage Hyperparameter optimization Data system Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | 2021 IEEE Aerospace Conference (50100). |
| DOI: | 10.1109/aero50100.2021.9438220 |
| Popis: | This paper proposes an automated solution system to assist with Real-Time Operations and automatically identify and report on issues with data transfer, archive, and manipulation throughout the Ground Data System (GDS) process. As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The prior state of GDS issue detection and resolution was largely manual. GDSAs receive email alerts when something goes wrong, but it's not always clear what the exact problem is or how to fix it. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implemented various supervised machine learning-based models and evaluate their accuracies to identify a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in near real time. |
| Databáze: | OpenAIRE |
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