Automatic root cause analysis in manufacturing: an overview & conceptualization

Eduardo Oliveira (), Vera L. Miguéis and José L. Borges
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Eduardo Oliveira: INESC TEC, Faculty of Engineering of the University of Porto - Rua Dr. Roberto Frias Edifício I
Vera L. Miguéis: INESC TEC, Faculty of Engineering of the University of Porto - Rua Dr. Roberto Frias Edifício I
José L. Borges: INESC TEC, Faculty of Engineering of the University of Porto - Rua Dr. Roberto Frias Edifício I

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 5, No 1, 2078 pages

Abstract: Abstract Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

Keywords: Root cause analysis; Manufacturing; Fault diagnosis; Data mining; Machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-01914-3

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