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A Simulator and Training Technique for Diagnosing Plant Failures from Control Panels.

K D Duncan1, A Shepherd1

  • 1a Department of Psychology , University of Hull.

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This study developed a plant failure identification simulator and training technique. The new method significantly improved diagnostic accuracy and reduced stress in trainees, enhancing operational safety.

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Area of Science:

  • Human Factors Engineering
  • Industrial Psychology
  • Cognitive Science

Background:

  • Traditional methods for identifying plant failures from control panel indications face simulation fidelity challenges.
  • Existing signal identification techniques may bias problem-solving descriptions by requiring serial information processing.
  • Operator stress during diagnosis is a critical factor influenced by environmental hazards and time constraints.

Purpose of the Study:

  • To develop a simulator and training technique for plant failure identification from control panel indications.
  • To address simulation fidelity issues related to input features, operator stress, and diagnostic time constraints.
  • To evaluate the effectiveness of the developed training technique in improving diagnostic accuracy and reducing stress.

Main Methods:

  • Development of a simulator using life-size projection of control panel mock-ups.
  • Implementation of an adaptive cumulative training regime with a high volume of failure array exposure.
  • Performance assessment through testing 24 hours post-training, analyzing diagnostic strategies and error rates.

Main Results:

  • Trainees demonstrated high diagnostic accuracy, with most making no or minimal errors in post-training tests.
  • The training regime allowed trainees to process significantly more failure arrays in a shorter time compared to operational settings.
  • Subject reports revealed diverse diagnostic strategies, including pattern recognition and heuristic-based instrument checking.

Conclusions:

  • The developed simulator and training technique are effective in enhancing plant failure identification skills.
  • The training approach appears to reduce diagnostic stress by enabling performance with fewer discriminatory dimensions.
  • Improved diagnostic robustness in dangerous environments is achievable by minimizing the dimensions required for accurate fault identification.