1,069 research outputs found

    ARPHA: an FDIR architecture for Autonomous Spacecrafts based on Dynamic Probabilistic Graphical Models

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    This paper introduces a formal architecture for on-board diagnosis, prognosis and recovery called ARPHA. ARPHA is designed as part of the ESA/ESTEC study called VERIFIM (Veri\ufb01cation of Failure Impact by Model checking). The goal is to allow the design of an innovative on-board FDIR process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial observability and detection of recovery actions taking into account imminent failures. We show how the model needed by ARPHA can be built through a standard fault analysis phase, \ufb01nally producing an extended version of a fault tree called EDFT; we discuss how EDFT can be adopted as a formal language to represent the needed FDIR knowledge, that can be compiled into a corresponding Dynamic Decision Network to be used for the analysis. We also discuss the software architecture we are implementing following this approach, where on-board FDIR can be implemented by exploiting on-line inference based on the junction tree approach typical of probabilisticgraphical models

    Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

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    Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.Comment: 7 pages, 2 figures. Accepted at the 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS 202

    An Anomaly Detection Method for Satellites Using Monte Carlo Dropout

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    Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several Long Short-Term Memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a post-processing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives

    An integrated diagnostic architecture for autonomous robots

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    Abstract unavailable please refer to PD

    Artificial Intelligence for Small Satellites Mission Autonomy

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    Space mission engineering has always been recognized as a very challenging and innovative branch of engineering: since the beginning of the space race, numerous milestones, key successes and failures, improvements, and connections with other engineering domains have been reached. Despite its relative young age, space engineering discipline has not gone through homogeneous times: alternation of leading nations, shifts in public and private interests, allocations of resources to different domains and goals are all examples of an intrinsic dynamism that characterized this discipline. The dynamism is even more striking in the last two decades, in which several factors contributed to the fervour of this period. Two of the most important ones were certainly the increased presence and push of the commercial and private sector and the overall intent of reducing the size of the spacecraft while maintaining comparable level of performances. A key example of the second driver is the introduction, in 1999, of a new category of space systems called CubeSats. Envisioned and designed to ease the access to space for universities, by standardizing the development of the spacecraft and by ensuring high probabilities of acceptance as piggyback customers in launches, the standard was quickly adopted not only by universities, but also by agencies and private companies. CubeSats turned out to be a disruptive innovation, and the space mission ecosystem was deeply changed by this. New mission concepts and architectures are being developed: CubeSats are now considered as secondary payloads of bigger missions, constellations are being deployed in Low Earth Orbit to perform observation missions to a performance level considered to be only achievable by traditional, fully-sized spacecraft. CubeSats, and more in general the small satellites technology, had to overcome important challenges in the last few years that were constraining and reducing the diffusion and adoption potential of smaller spacecraft for scientific and technology demonstration missions. Among these challenges were: the miniaturization of propulsion technologies, to enable concepts such as Rendezvous and Docking, or interplanetary missions; the improvement of telecommunication state of the art for small satellites, to enable the downlink to Earth of all the data acquired during the mission; and the miniaturization of scientific instruments, to be able to exploit CubeSats in more meaningful, scientific, ways. With the size reduction and with the consolidation of the technology, many aspects of a space mission are reduced in consequence: among these, costs, development and launch times can be cited. An important aspect that has not been demonstrated to scale accordingly is operations: even for small satellite missions, human operators and performant ground control centres are needed. In addition, with the possibility of having constellations or interplanetary distributed missions, a redesign of how operations are management is required, to cope with the innovation in space mission architectures. The present work has been carried out to address the issue of operations for small satellite missions. The thesis presents a research, carried out in several institutions (Politecnico di Torino, MIT, NASA JPL), aimed at improving the autonomy level of space missions, and in particular of small satellites. The key technology exploited in the research is Artificial Intelligence, a computer science branch that has gained extreme interest in research disciplines such as medicine, security, image recognition and language processing, and is currently making its way in space engineering as well. The thesis focuses on three topics, and three related applications have been developed and are here presented: autonomous operations by means of event detection algorithms, intelligent failure detection on small satellite actuator systems, and decision-making support thanks to intelligent tradespace exploration during the preliminary design of space missions. The Artificial Intelligent technologies explored are: Machine Learning, and in particular Neural Networks; Knowledge-based Systems, and in particular Fuzzy Logics; Evolutionary Algorithms, and in particular Genetic Algorithms. The thesis covers the domain (small satellites), the technology (Artificial Intelligence), the focus (mission autonomy) and presents three case studies, that demonstrate the feasibility of employing Artificial Intelligence to enhance how missions are currently operated and designed

    Reconfigurable High Performance Secured NoC Design Using Hierarchical Agent-based Monitoring System

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    With the rapid increase in demand for high performance computing, there is also a significant growth of data communication that leads to leverage the significance of network on chip. This paper proposes a reconfigurable fault tolerant on chip architecture with hierarchical agent based monitoring system for enhancing the performance of network based multiprocessor system on chip against faulty links and nodes. These distributed agents provide healthy status and congestion information of the network. This status information is used for further packet routing in the network with the help of XY routing algorithm. The functionality of Agent is enhanced not only to work as information provider but also to take decision for packet to either pass or stop to the processing element by setting the firewall in order to provide security. Proposed design provides a better performance and area optimization by avoiding deadlock and live lock as compared to existing approaches over network design

    A Hierarchical Model-Based Reasoning Approach for Fault Diagnosis in Multi-Platform Space Systems

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    Health monitoring and fault diagnosis in traditional single spacecraft missions are mostly accomplished by human operators on ground through around-the-clock monitoring and trend analysis on huge amount of telemetry data. Future multiplatform space missions, commonly known as the formation flight missions, will utilize multiple inexpensive spacecraft in formation by distributing the functionalities of a single platform among the miniature inexpensive platforms. Current spacecraft diagnosis practices do not scale up well for multiple space platforms due to an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team which will be large if traditional diagnosis is employed. An ideal solution to this problem is to incorporate an autonomous fault detection, isolation, and recovery (FDIR) mechanism. However, the effectiveness of spacecraft autonomy is yet to be demonstrated and due to the existence of perceived risks, it is often desired that the expert human operators be involved in the spacecraft operations and diagnosis processes i.e., the autonomous spacecraft actions be understandable by the human operators on ground so that intervention may be made, if necessary. To address the above problems and requirements, in this research a systematic and transparent fault diagnosis methodology for ground-based operations of multi-platform space systems is developed. First, novel hierarchical fault diagnosis concepts and framework are developed. Within this framework, a multi-platform space system is decomposed hierarchically into multiple levels. The decomposition is driven by the need for supporting the development of the components/subsystems of the overall system by a number of design teams and performing integration at the end. A multi-platform system is considered to be a set of interacting components where components at different levels correspond to formation, system, sub-system, etc. depending on the location of the node in the hierarchy. Two directed graph based fault diagnosis models are developed namely, fuzzy rule based hierarchical fault diagnosis model (HFDM), and Bayesian networks (BN)-based component dependency model (CDM). In HFDM, fault diagnosis of different components in the formation flight is investigated. Fuzzy rules are developed for fault diagnosis at different levels in the hierarchy by taking into account the uncertainties in the fault manifestations in a given component. In this model, the component interactions are quantified without taking the uncertainties in the component health state dependencies into account. Next, a component dependency model (CDM) based on Bayesian networks (BN) models is developed in order to take the uncertainties in component dependencies into account. A novel methodology for identifying CDM parameters is proposed. Fault evidences are introduced to the CDM when the fault modes of a component are observed via fuzzy rule activations. Advantages and limitations associated with the proposed HFDM and the CDM are also discussed. Finally, the verification and validation (V&V) of the hierarchical diagnosis models are investigated via a sensitivity analysis approach. It should be noted that the proposed methodology and the fault diagnosis strategies and algorithms that are developed in this research are generic in a sense that they can be applied to any hierarchically decomposable complex systems. However, the system and domain specific knowledge they require, especially for modeling component dependencies, are mostly available in the aerospace industry where extensive system design and integration-related analysis are common due to high system building cost and failure risks involved

    Application of advanced technology to space automation

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    Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

    Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight

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    Current spacecraft health monitoring and fault-diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry data achieved by employing machine learning and reasoning algorithms has been pointed out in the literature for enhancing diagnostic performance and assisting the less-experienced personnel in performing monitoring and diagnosis tasks. In this paper, we develop a systematic and transparent fault-diagnosis methodology within a hierarchical fault-diagnosis framework for a satellites formation flight. We present our proposed hierarchical decomposition framework through a novel Bayesian network, whose structure is developed from the knowledge of component health-state dependencies. We have developed a methodology for specifying the network parameters that utilizes both node fault-diagnosis performance data and domain experts' beliefs. Our proposed model development procedure reduces the demand for expert's time in eliciting probabilities significantly. Our proposed approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight architecture. Although our proposed approach is developed from the satellite fault-diagnosis perspective, it is generic and is targeted toward other types of cooperative fleet vehicle diagnosis problems
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