A sensor selection method for fault diagnostics
In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system....
| Main Authors: | , , |
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| Format: | Book Section |
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CRC Press
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41102/ |
| _version_ | 1848796197013159936 |
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| author | Reeves, J. Remenyte-Prescott, Rasa Andrews, John |
| author_facet | Reeves, J. Remenyte-Prescott, Rasa Andrews, John |
| author_sort | Reeves, J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system. Therefore, sensors need to be selected based on the information that they provide.
In this paper, a sensor selection process is introduced based on a novel sensor performance metric. In this process, sensors are selected based on their ability to detect faults and diagnose failures of components in the system, as well as the severity of failure effects on system performance. A Bayesian Belief Network (BBN) is used to model the outputs of the sensors. Sensor reading evidence is introduced in the BBN to enable the component failures to be identified. A simple system example is used to illustrate the proposed approach. |
| first_indexed | 2025-11-14T19:44:09Z |
| format | Book Section |
| id | nottingham-41102 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:44:09Z |
| publishDate | 2017 |
| publisher | CRC Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-411022020-05-04T18:47:08Z https://eprints.nottingham.ac.uk/41102/ A sensor selection method for fault diagnostics Reeves, J. Remenyte-Prescott, Rasa Andrews, John In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system. Therefore, sensors need to be selected based on the information that they provide. In this paper, a sensor selection process is introduced based on a novel sensor performance metric. In this process, sensors are selected based on their ability to detect faults and diagnose failures of components in the system, as well as the severity of failure effects on system performance. A Bayesian Belief Network (BBN) is used to model the outputs of the sensors. Sensor reading evidence is introduced in the BBN to enable the component failures to be identified. A simple system example is used to illustrate the proposed approach. CRC Press 2017-05-25 Book Section PeerReviewed Reeves, J., Remenyte-Prescott, Rasa and Andrews, John (2017) A sensor selection method for fault diagnostics. In: Safety and Reliability – Theory and Application: ESREL 2017. CRC Press. ISBN 9781138629370 https://www.crcpress.com/ESREL-2017-Portoroz-Slovenia-18-22-June-2017/Cepin-Bris/p/book/9781138629370 |
| spellingShingle | Reeves, J. Remenyte-Prescott, Rasa Andrews, John A sensor selection method for fault diagnostics |
| title | A sensor selection method for fault diagnostics |
| title_full | A sensor selection method for fault diagnostics |
| title_fullStr | A sensor selection method for fault diagnostics |
| title_full_unstemmed | A sensor selection method for fault diagnostics |
| title_short | A sensor selection method for fault diagnostics |
| title_sort | sensor selection method for fault diagnostics |
| url | https://eprints.nottingham.ac.uk/41102/ https://eprints.nottingham.ac.uk/41102/ |