Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

作者：Zixin Wang, Mohammad R. Jahanshahi, Mohsen Azimi, Shirley J. Dyke · 发表于：AIAA Journal · 年份：2024 · DOI：10.2514/1.j063815 · 被引用次数：6 · 研究领域：Planetary Science and Exploration、Image Processing and 3D Reconstruction、Anomaly Detection Techniques and Applications

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the...