An Experimental Study of the Effect of Patrol Inspection Strategy for Improving Detection Rate of Abnormality of Industrial Plants



Published Sep 4, 2023
Akio Gofuku Riku Kishimoto


Safe and stable operation is very important in large-scale plants. Patrol inspections are comprehensive and frequent on-site inspections and are indispensable works because early detection of a malfunction or failure of a component is desirable to achieve safe and stable operations. This study supposed that the detection rate of anomalies by patrol inspections is related with not only the amount of knowledge about plants but also the modalities such as vision and listening that are conscious during inspections. This study experimentally examined the difference in the detection rate of anomalies depending on patrol inspection strategies. In the experiment, a simulated small plant using the devices that are similar to plant components was constructed as a site for patrol inspections, and two types of patrol inspection strategies were examined: visual strategy that focuses on watching component conditions and auditory strategy that focuses on listening abnormal sounds. Fifteen volunteers participated in the experiment. The relationship between patrol inspection strategies and the approach and performance of patrol inspections were investigated by observing patrol inspection behaviors of participants and measuring their anomaly detection rates. As the results, although there was a small difference in the detection rate for visually detectable anomalies, the effect of the auditory strategy was suggested to improve the detection rate for not only auditory detectable anomalies but also visually detectable anomalies.

Abstract 75 | PDF Downloads 81



patrol inspection, large-scale industrial plant, abnormality detection, inspection strategy

Yokota, D., et al., (2021). Analyzing a Structure of Skilled Knowledge for Plant Patrol Inspection. 2021 Spring Meeting of The Japan Society for Precision Engineering. pp. 33-34. (In Japanese)

Redmon, J., Divvala, S., Girshick, R., Farhadi, A, (2016). You Only Look Once: Unified, Real-Time Object Detection. Cornell University arXiv:1506.02604.
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