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

Abstract

The paper describes (1) the development of a simulator and (2) the first results of a training technique for the identification of plant failures from control panel indications. Input or signal features of the task present more simulation fidelity problems than its response or output features. Current, techniques for identifying effective signals, e.g. ‘ blanking-off ’ information, or protocol analysis, bias any description of problem solving since they require serial reporting, if not serial collection, of information by the operator. They also require inferences as to what is an effective item of information. It is therefore argued that simulation should preserve all those features which may in principle provide, or influence acquisition of, diagnostic information, specifically panel layout, instrument design and display size. Further fidelity problems are the stress from operating in a dangerous environment; stress from hazards or sanctions following mistaken diagnosis; and the stress of diagnosing in a short time interval. The simulator uses bock-projection to life size of slides of control panel mock-ups by a random access projector. Under an adaptive cumulative part regime, trainees saw on average 89 failure arrays in 30 min, an obvious advantage over the operational situation. In a test 24 hr after training, consisting of the eight faults each presented four times in random order, 4 out of 17 trainees made only one error in 32 diagnosos; the other trainees performed perfectly. Subjects' reports indicate very different solution strategies, e.g., recognition of alarm patterns; serial instrument checking determined by heuristics of plant functioning. Several features of performance arc consistent with the view that trainees use a minimal number of dimensions for correct discrimination and that these change as the number of different fault arrays increases. It is argued that this training regime should reduce stress. In particular it is argued that, according to current theories of stress, the fewer dimensions needed for diagnosis, the more robust will be diagnostic performance in dangerous environments.