Space Habitat Monitoring

Natalia Criollo

Co-Presenters: Xyanna Fuentes

College: Hennings College of Science Mathematics and Technology

Major: BS.COMPUTER/SCI

Faculty Research Mentor: Moitrayee Chatterjee  

Abstract:

Title: Monitoring and Detecting Anomalies during Space ExplorationAuthor: Xyanna Fuentes, Natalia Criollo, Department of Computer Science, New Jersey City UniversityAbstract:Deep-space exploration requires more independence and autonomy as Earth communications and support can become increasingly less viable the further away missions are. Additionally, spacecraft would need to be able to detect, handle, and avoid potential anomalies during such missions. Previous studies have focused on potential design considerations to create fully autonomous systems that are capable of monitoring and maintenance within spacecraft. The purpose of this research is to create a model application that could assist in monitoring as well as detecting anomalies within space habitats during space exploration/missions.Main methods involve building machine learning models whilst utilizing datasets such as the Astronaut and Mission dataset from Kaggle and Air Quality Prediction dataset from HuggingFace for training and testing purposes. The models will be trained for environmental and life-support systems monitoring. This includes temperature, humidity, CO2 and O2 levels, radiation, pressure, sleep-quality, stress-levels, and power usage. Through this research, it aims to provide some assistance in the development of self-sufficient systems for deep-space exploration.Keywords: Monitoring, Detecting Anomalies, Space Habitats, Space Exploration, Machine Learning