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A fuzzy logical vigilance alarm system for improving situation awareness and trust in supervisory control

An automation system's operating performance is judged by how well an automation unit is monitored and maintained by its supervisors. Previous research has shown that situation awareness (SA) and trust are critical factors in automation. The purpose of this study was to evaluate and improve sup...

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Bibliographic Details
Published in:Human factors and ergonomics in manufacturing & service industries 2006-09, Vol.16 (4), p.409-426
Main Authors: Liu, Cheng-Li, Su, Kuo-Wei
Format: Article
Language:English
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Summary:An automation system's operating performance is judged by how well an automation unit is monitored and maintained by its supervisors. Previous research has shown that situation awareness (SA) and trust are critical factors in automation. The purpose of this study was to evaluate and improve supervisory performance in automation manufacturing. First, a conceptual structure of the relationship among SA, trust, and vigilance was developed. Second, a quantitative vigilance performance‐measuring model (η value) was proposed. Third, a matrix experiment based on orthogonal arrays through a simulated system of an auxiliary feed‐water system (AFWS) was conducted to verify the effect of the measuring model. Finally, according to the vigilance performance‐measuring model, a fuzzy logical vigilance alarm system was constructed to improve operating performance. The results of the first experiment indicated that the η value on human dynamic decision‐making characteristics was easy and objective in the measurement of operators' vigilance. With greater vigilance, there is a greater likelihood of making appropriate SA and acquiring more trust in automation. The results of the second experiment indicated that applying the η value to the design of the fuzzy logical vigilance alarm system could improve supervisory performance efficiently. Therefore, an adaptive vigilance performance‐measuring model combined with a fuzzy technique applied to the design of a human–machine interface for the improvement of cognitive decision making and operating performance is an important new direction in automation manufacturing. © 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 409–426, 2006.
ISSN:1090-8471
1520-6564
DOI:10.1002/hfm.20056