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Open AccessArticle

Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

1
Doctoral School of Mechanical Engineering, Szent Istvan University, 2100 Godollo, Hungary
2
Institute of Engineering Management, Szent Istvan University, 2100 Godollo, Hungary
*
Author to whom correspondence should be addressed.
Processes 2020, 8(2), 224; https://doi.org/10.3390/pr8020224 (registering DOI)
Received: 9 January 2020 / Revised: 3 February 2020 / Accepted: 12 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and effects analysis in a more efficient way as compared to the conventional techniques.
Keywords: Industry 4.0; auto machine learning; failure mode effects analysis; risk priority number Industry 4.0; auto machine learning; failure mode effects analysis; risk priority number
MDPI and ACS Style

Sader, S.; Husti, I.; Daróczi, M. Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry. Processes 2020, 8, 224.

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