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  4. Vol 4, Issue 3, 2023

Research on advanced manufacturing process monitoring and fault prediction method based on machine learning

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DOI: 10.23977/autml.2023.040311 | Downloads: 10 | Views: 256

Author(s)

Xinrui Wang 1, Hang Shang 1, Ning Li 1

Affiliation(s)

1 College of Arts & Information Engineering, Dalian Polytechnic University, Dalian, Liaoning, 116400, China

Corresponding Author

Hang Shang

ABSTRACT

With the rapid development of advanced manufacturing technology, efficient monitoring and fault prediction of manufacturing process has become an important link to realize intelligent manufacturing. The purpose of this study is to explore the advanced manufacturing process monitoring and fault prediction method based on machine learning, so as to improve the stability and reliability of the production process. In the aspect of monitoring, by effectively integrating multi-source data and adopting advanced data preprocessing methods, the real-time monitoring of manufacturing process state can be realized, which provides strong support for finding anomalies in time. Aiming at fault prediction, this study discusses the performance of support vector machine (SVM) in different manufacturing environments, and proposes a fault prediction method based on improved SVM to improve the prediction accuracy and generalization ability. Through empirical research, the proposed method is verified. The training result diagram clearly shows the relationship among support vector, training error pipeline and regression approximation results, while the prediction result diagram shows the excellent performance of the model on test data. These research results have important theoretical and practical value for promoting the development of intelligent manufacturing and improving the intelligent level of production process.

KEYWORDS

Machine learning; fault prediction; monitoring; advanced manufacturing

CITE THIS PAPER

Xinrui Wang, Hang Shang, Ning Li, Research on advanced manufacturing process monitoring and fault prediction method based on machine learning. Automation and Machine Learning (2023) Vol. 4: 87-92. DOI: http://dx.doi.org/10.23977/autml.2023.040311.

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