Sensor selection for fault diagnostics using performance metric

J Reeves, R Remenyte-Prescott and J Andrews

Journal of Risk and Reliability, 2019, vol. 233, issue 4, 537-552

Abstract: As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected. In this article, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the Bayesian Belief Network. The results of the Bayesian Belief Network can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this article.

Keywords: Sensor selection; Bayesian belief network; fault diagnostics (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X18804690 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:233:y:2019:i:4:p:537-552

DOI: 10.1177/1748006X18804690

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().