Data or interpretations: Impacts of information presentation strategies on diagnostic processes. Issue 4 (6th March 2020)

• Record Type: Journal Article
• Authors: Müller, Romy; Gögel, Christina; Bönsel, Rica
• Is Part Of: Human factors and ergonomics in manufacturing and service industries. Volume 30:Issue 4(2020)
• Page Start: 266
• Page End: 281
• Publication Date: 2020-03-06
• Subjects: automation bias -- degree of automation -- fault diagnosis -- information sampling -- operator assistance; Computer integrated manufacturing systems -- Periodicals; Human engineering -- Periodicals; 620.8205
• DOI: 10.1002/hfm.20838
• Languages: English
• ISSNs: 1090-8471

Abstract

Abstract: 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.