research-article

Anomaly Detection of Bolt Tightening Process Based on Improved SMOTE

Authors:

Quanchao JiaAuthors Info & Claims

ICRAI '20: Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

Pages 9 - 14

https://doi.org/10.1145/3449301.3449304

Published: 09 June 2021 Publication History

Abstract

For some industrial production processes, deep fault can be detected by data mining and data analytics of the process data. This can help to get a higher level of production quality. Anomaly detection of bolt tightening process was studied in this paper. Imbalanced data set is the main difficulty in this problem. An improved synthetic minority over-sampling technique (SMOTE) algorithm is proposed based on density-based spatial clustering of applications with noise (DBSCAN). By oversampling within-class imbalanced samples, the improved SMOTE algorithm can overcome the shortcomings of the traditional SMOTE method and can retain more sample features. As for the model feature extraction and classification, the sample classifier is trained by the Xgboost algorithm. An Experiment is carried out on a factory's real data set, which shows that the improved SMOTE algorithm can help to achieve great classification performance promotion.

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Digital Library

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Digital Library

Cited By

Morais TDomingos TFalcão JCamacho MMarques ANeves ILopes HTeixeira R(2024)Detecting Soil Tillage in Portugal: Challenges and Insights from Rules-Based and Machine Learning Approaches Using Sentinel-1 and Sentinel-2 DataSustainability10.3390/su16231038916:23(10389)Online publication date: 27-Nov-2024
https://doi.org/10.3390/su162310389

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cover image ACM Other conferences

ICRAI '20: Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

November 2020

288 pages

ISBN:9781450388597

DOI:10.1145/3449301

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 June 2021

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ICRAI 2020

ICRAI 2020: 2020 6th International Conference on Robotics and Artificial Intelligence

November 20 - 22, 2020

Singapore, Singapore

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Cited By

Morais TDomingos TFalcão JCamacho MMarques ANeves ILopes HTeixeira R(2024)Detecting Soil Tillage in Portugal: Challenges and Insights from Rules-Based and Machine Learning Approaches Using Sentinel-1 and Sentinel-2 DataSustainability10.3390/su16231038916:23(10389)Online publication date: 27-Nov-2024
https://doi.org/10.3390/su162310389

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