Analysis of Artificial Intelligence based diagnostic methods for satellites

The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed.The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Reason-Resilience and security of geospatial data for critical infrastructures

Peer reviewe

Autonomous Neuro-Fuzzy Solution for Fault Detection and Attitude Control of a 3U Cubesat

In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, algorithms involving artificial intelligence (i.e. neural networks and fuzzy logics) have began to spread even in the space applications. Nowadays, thanks to these reasons, the implementation of such techniques is becoming realizable even on smaller platforms, such as CubeSats. The paper presents an algorithm for the fault detection and for the fault-tolerant attitude control of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves fuzzy logic and multi-layer feed-forward online-trained neural network (percep- tron). It is utilized in a simulation of a CubeSat satellite placed in LEO, considering as available attitude con- trol actuators three magnetic torquers and one reaction wheel. In particular, fuzzy logics are used for the fault detection and isolation, while the neural network is employed for adapting the control to the perturbation introduced by the fault. The simulation is performed considering the attitude of the satellite known without measurement error. In addition, the paper presents the system, simulator and algorithm architecture, with a particular focus on the design of fuzzy logics (connection and implication operators, rules and input/output qualificators) and the neural network architecture (number of layers, neurons per layer), threshold and activation func- tions, offline and online training algorithm and its data management. With respect to the offline training, a model predictive controller has been adopted as supervisor. In con- clusion the paper presents the control torques, state variables and fuzzy output evolution, in the different faulty configurations. Results show that the implementation of the fuzzy logics joined with neural networks provide good ro- bustness, stability and adaptibility of the system, allowing to satisfy specified performance requirements even in the event of a malfunctioning of a system actuator