Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach

Daniele CODETTA RAITERI; Luigi PORTINALE; A Guiotto; Y. Yushtein

Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach

Daniele CODETTA RAITERI
, Luigi PORTINALE
, A Guiotto
, Y. Yushtein

Department of Science and Technological Innovation

Research output: Contribution to conference › Paper › peer-review

Abstract

Recent studies focused on the achievement of autonomy by spacecrafts, with the aim of avoiding the intervention of the ground control. In this sense, the ARPHA software prototype has been developed for the automatic failure detection, identification and recovery (FDIR), and is based on the on-board analysis of a Dynamic Bayesian Network (DBN) representing the system behaviour conditioned by the conditions of components and environment. In this paper, we describe the main functionalities of ARPHA, and we apply its FDIR capabilities to the power supply subsystem of an exploring rover, taking into account four scenarios leading to anomalies or failures. The DBN model of the system is described. Then, we test the execution of ARPHA, together with a rover simulator providing sensor data and plan data. In particular, we show the results of diagnosis, prognosis and recovery, returned by ARPHA when the scenarios occur.

Original language	English
Number of pages	8
Publication status	Published - 1 Jan 2012
Event	ISAIRAS 2012 - International Symposium on Artificial Intelligence, Robotics and Automation in Space - Torino Duration: 1 Jan 2012 → …

Conference

Conference	ISAIRAS 2012 - International Symposium on Artificial Intelligence, Robotics and Automation in Space
City	Torino
Period	1/01/12 → …

Keywords

Dynamic Bayesian Networks
Fault detection identification and recovery
Probabilistic Graphical Models

Cite this

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title = "Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach",

abstract = "Recent studies focused on the achievement of autonomy by spacecrafts, with the aim of avoiding the intervention of the ground control. In this sense, the ARPHA software prototype has been developed for the automatic failure detection, identification and recovery (FDIR), and is based on the on-board analysis of a Dynamic Bayesian Network (DBN) representing the system behaviour conditioned by the conditions of components and environment. In this paper, we describe the main functionalities of ARPHA, and we apply its FDIR capabilities to the power supply subsystem of an exploring rover, taking into account four scenarios leading to anomalies or failures. The DBN model of the system is described. Then, we test the execution of ARPHA, together with a rover simulator providing sensor data and plan data. In particular, we show the results of diagnosis, prognosis and recovery, returned by ARPHA when the scenarios occur.",

keywords = "Dynamic Bayesian Networks, Fault detection identification and recovery, Probabilistic Graphical Models, Dynamic Bayesian Networks, Fault detection identification and recovery, Probabilistic Graphical Models",

author = "\{CODETTA RAITERI\}, Daniele and Luigi PORTINALE and A Guiotto and Y. Yushtein",

year = "2012",

month = jan,

day = "1",

language = "English",

note = "ISAIRAS 2012 - International Symposium on Artificial Intelligence, Robotics and Automation in Space ; Conference date: 01-01-2012",

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Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach. / CODETTA RAITERI, Daniele ; PORTINALE, Luigi; Guiotto, A et al.
2012. Paper presented at ISAIRAS 2012 - International Symposium on Artificial Intelligence, Robotics and Automation in Space, Torino.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach

AU - CODETTA RAITERI, Daniele

AU - PORTINALE, Luigi

AU - Guiotto, A

AU - Yushtein, Y.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Recent studies focused on the achievement of autonomy by spacecrafts, with the aim of avoiding the intervention of the ground control. In this sense, the ARPHA software prototype has been developed for the automatic failure detection, identification and recovery (FDIR), and is based on the on-board analysis of a Dynamic Bayesian Network (DBN) representing the system behaviour conditioned by the conditions of components and environment. In this paper, we describe the main functionalities of ARPHA, and we apply its FDIR capabilities to the power supply subsystem of an exploring rover, taking into account four scenarios leading to anomalies or failures. The DBN model of the system is described. Then, we test the execution of ARPHA, together with a rover simulator providing sensor data and plan data. In particular, we show the results of diagnosis, prognosis and recovery, returned by ARPHA when the scenarios occur.

AB - Recent studies focused on the achievement of autonomy by spacecrafts, with the aim of avoiding the intervention of the ground control. In this sense, the ARPHA software prototype has been developed for the automatic failure detection, identification and recovery (FDIR), and is based on the on-board analysis of a Dynamic Bayesian Network (DBN) representing the system behaviour conditioned by the conditions of components and environment. In this paper, we describe the main functionalities of ARPHA, and we apply its FDIR capabilities to the power supply subsystem of an exploring rover, taking into account four scenarios leading to anomalies or failures. The DBN model of the system is described. Then, we test the execution of ARPHA, together with a rover simulator providing sensor data and plan data. In particular, we show the results of diagnosis, prognosis and recovery, returned by ARPHA when the scenarios occur.

KW - Dynamic Bayesian Networks

KW - Fault detection identification and recovery

KW - Probabilistic Graphical Models

KW - Dynamic Bayesian Networks

KW - Fault detection identification and recovery

KW - Probabilistic Graphical Models

UR - https://iris.uniupo.it/handle/11579/29043

M3 - Paper

T2 - ISAIRAS 2012 - International Symposium on Artificial Intelligence, Robotics and Automation in Space

Y2 - 1 January 2012

ER -

Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach

Abstract

Conference

Keywords

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