Visible to the public Research on Trust Degree Model of Fault Alarms Based on Neural Network

TitleResearch on Trust Degree Model of Fault Alarms Based on Neural Network
Publication TypeConference Paper
Year of Publication2018
AuthorsMa, S., Zeng, S., Guo, J.
Conference Name2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS)
Date PublishedOct. 2018
ISBN Number978-1-5386-7076-7
Keywordsalarm errors, alarm system, Alarm systems, available experimental studies, Biological neural networks, Collaboration, compliance, computerised instrumentation, data base, Databases, decision making, false alarm rate, false trust, fault alarms, fault diagnosis, Human Behavior, human factors, human machine system, human trust, neural nets, Neural Network, Neurons, operator factors, policy-based governance, pubcrawl, reliability, reliance, resilience, Resiliency, Scalability, security of data, situational factors, Training, trust degree model

False alarm and miss are two general kinds of alarm errors and they can decrease operator's trust in the alarm system. Specifically, there are two different forms of trust in such systems, represented by two kinds of responses to alarms in this research. One is compliance and the other is reliance. Besides false alarm and miss, the two responses are differentially affected by properties of the alarm system, situational factors or operator factors. However, most of the existing studies have qualitatively analyzed the relationship between a single variable and the two responses. In this research, all available experimental studies are identified through database searches using keyword "compliance and reliance" without restriction on year of publication to December 2017. Six relevant studies and fifty-two sets of key data are obtained as the data base of this research. Furthermore, neural network is adopted as a tool to establish the quantitative relationship between multiple factors and the two forms of trust, respectively. The result will be of great significance to further study the influence of human decision making on the overall fault detection rate and the false alarm rate of the human machine system.

Citation Keyma_research_2018