Pipe Corrosion Inspection System based on Human-in-the-Loop Machine Learning

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Published Sep 4, 2023
Toshihiro Shimbo Yousuke Okada Hitoshi Matsubara

Abstract

The aim of this study was to improve the efficiency of external corrosion inspection of pipes in chemical plants. Currently, the preferred method involves manual examination of images of corroded pipes; however, this places significant workload on human experts owing to the very high number of such images. To address this issue, we developed an artificial intelligence (AI)-based corrosion diagnosis system and implemented it in a factory.

Initially, interviews were conducted to understand the decision-making processes of human experts. Subsequently, we converted their tacit knowledge into explicit knowledge, which was used to define the training data for the machine learning (ML) model. The predictions of the ML model were compared with the manually obtained results, exhibiting an accuracy of 70 %.

The proposed architecture was based on human-in-the-loop ML. It included a process to retrain the ML model using manual results gathered during operation. It was operated using a collaborative approach, in which human experts supported the ML model under development.

The proposed model enhanced the efficiency of the inspection process successfully. 

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Keywords

human in the loop machine learning, collaboration between human and AI, AI corrosion diagnosis system, system architecture

References
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Christos E, Petros P, Luka B, Vassilis K, Apostolos F, Christos K, Cristobal R (2019). Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems, Annual Reviews in Control 47, pp. 249–265

Hata M (2019). Corrosion Diagnosis System for Pipe Outer Surface by Image Data Analysis, Accenture, 49th Petroleum and Petrochemical Conference (Yamagata Conference) (in Japanese)

Tanaka T, Takeda K (2020). Development of a Management Target Selection Method by Automatic Recognition of Pipes Using AI, Shizuoka University, 63rd Joint Automatic Control Conference (in Japanese)
Section
Special Session Papers