Industrial fault diagnosis can be supported by assistance systems that infer fault causes from sensor data. The present study asked what information these algorithms should make available to operators. In a computer‐based experiment about fault diagnosis in a packaging machine, three information presentation strategies were compared regarding their impacts on information sampling, performance, and knowledge acquisition: Providing only sensor data, sensor data along with three possible interpretations, or only the most likely interpretation. Before submitting a diagnosis, participants could sample process parameters, one of which indicated the fault cause. We hypothesized that providing only sensor data would lead to more parameter checking and slower solutions than interpretations. While providing only one interpretation was expected to enable efficient performance for correct interpretations, it should lead to either of two types of performance costs for incorrect interpretations: Errors if participants refrain from checking parameters, or slowdowns in performance if they keep on checking. The results confirmed that participants with only sensor data performed inefficiently. Participants with only one interpretation thoroughly checked parameters but still were fastest when the interpretation was correct, while when it was incorrect they were three times slower than participants with only sensor data. Participants with three interpretations (one of which was always correct) performed almost as efficiently as those with only one correct interpretation. The results indicate that highly preprocessed information leads to efficient performance when it is correct but prevents learning about fault causes. Overall, providing several possible interpretations seemed to be the best strategy.

中文翻译：

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数据或解释：信息呈现策略对诊断过程的影响

辅助系统可以支持工业故障诊断，该辅助系统可以根据传感器数据推断出故障原因。本研究询问这些算法应为操作员提供哪些信息。在基于计算机的包装机故障诊断实验中，比较了三种信息呈现策略对信息采样，性能和知识获取的影响：仅提供传感器数据，传感器数据以及三种可能的解释，或者仅提供最多可能的解释。在提交诊断之前，参与者可以对过程参数进行采样，其中之一指示故障原因。我们假设仅提供传感器数据将比解释导致更多的参数检查和较慢的解决方案。虽然只提供一种解释可以实现正确解释的有效性能，但是对于错误解释，这将导致两种类型的性能成本中的一种：如果参与者不检查参数就会出错，或者如果他们继续检查就会降低性能。结果证实，仅具有传感器数据的参与者表现不佳。仅具有一种解释的参与者彻底检查了参数，但是当解释正确时仍然最快，而当解释不正确时，它们比仅具有传感器数据的参与者慢三倍。具有三种解释（其中一种总是正确的）的参与者的表现几乎与只有一种正确解释的参与者一样有效。结果表明，高度预处理的信息在正确时会带来高效的性能，但会阻止了解故障原因。总体而言，提供几种可能的解释似乎是最好的策略。