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Fault Diagnosis: Human Performance in the Digital and Automation Context

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Human-Automation Interaction

Part of the book series: Automation, Collaboration, & E-Services ((ACES,volume 10))

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Abstract

Diagnosis is a cognitively difficult task for operators working in the control rooms of complex industrial systems. The complexity of contemporary industrial systems increases with the application of digital and automation technologies, making fault diagnosis of such systems even more challenging to human operators. This chapter proposes a framework for human factors research on fault diagnosis, including diagnosis models and strategies, factors influencing human diagnosis performance, and methods to improve diagnosis task performance. As a general idea, automation should facilitate, help, and even collaborate with human operators to perform fault diagnosis.

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Authors and Affiliations

  1. Nuclear Power Institute of China, Chengdu, China

    Xi Lyu

  2. Tsinghua University, Beijing, China

    Manrong She, Xiaojun Wu, Kejin Chen & Zhizhong Li

  3. University of Science and Technology Beijing, Beijing, China

    Dan Pan

Authors
  1. Xi Lyu
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  2. Manrong She
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  3. Dan Pan
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  4. Xiaojun Wu
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  5. Kejin Chen
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  6. Zhizhong Li
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Corresponding author

Correspondence to Zhizhong Li .

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Editors and Affiliations

  1. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

  2. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Mark Lehto

  3. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Yuehwern Yih

  4. Department of Psychological Science, Purdue University, West Lafayette, IN, USA

    Robert W. Proctor

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Lyu, X., She, M., Pan, D., Wu, X., Chen, K., Li, Z. (2023). Fault Diagnosis: Human Performance in the Digital and Automation Context. In: Duffy, V.G., Lehto, M., Yih, Y., Proctor, R.W. (eds) Human-Automation Interaction. Automation, Collaboration, & E-Services, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-031-10780-1_14

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