A sensor selection method for fault diagnostics
Reeves, J.; Remenyte-Prescott, Rasa; Andrews, John
RASA REMENYTE-PRESCOTT R.REMENYTE-PRESCOTT@NOTTINGHAM.AC.UK
JOHN ANDREWS email@example.com
Professor of Infrastructure Asset Management
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.
Reeves, J., Remenyte-Prescott, R., & Andrews, J. (2017). A sensor selection method for fault diagnostics. In Safety and Reliability – Theory and Application: ESREL 2017. CRC Press
|Acceptance Date||Feb 1, 2017|
|Publication Date||May 25, 2017|
|Deposit Date||Mar 6, 2017|
|Publicly Available Date||May 25, 2017|
|Peer Reviewed||Peer Reviewed|
|Book Title||Safety and Reliability – Theory and Application: ESREL 2017|
|Additional Information||Conference paper from ESREL 2017, June 18-22, Slovenia.
Papers presented at ESREL 2017 will be published in the conference proceedings issued by Taylor and Francis, CRC Press: Safety and Reliability – Theory and Application: ESREL 2017. https://www.crcpress.com/ESREL-2017-Portoroz-Slovenia-18-22-June-2017/Cepin-Bris/p/book/9781138629370
A sensor selection method for fault diagnostics.pdf
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