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An ensemble unsupervised method for anomaly detection in industrial production data
Corresponding Author(s) : DACIAN GOINA
Student Thinkers and Advanced Research,
Vol. 3 No. 1 (2024): Proceedings of the 7th International Conference XGEN
Abstract
Working machines are largely used in industrial environments for production of items and generate lots of data following these processes. The proper operation of machines influence the production output, thus detection of anomalies in machines activities is a crucial thing for avoiding awful outcomes. This paper present an ensemble unsupersived anomaly detection method able to handle aspects such as efficiency and data volume. Proposed method consists of 2 stages: in the first stage, statistical-based methods are used to assign labels to input data, then second stage use feature bagging technique to create and train estimators later used for prediction.
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- P. K. Shabad, A. Alrashide and M. Osama, "Anomaly Detection in Smart Grids using Machine Learning," 2021.
- S. S. Aljameel, D. M. Alomari, S. Alismail, F. Khawaher, A. A. Alkhudhair, F. Aljubran and R. M. Alzannan, "An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning," Computation, vol. 10, no. 8, 2022.
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- L. Yunxiao, L. Youfang, X. QinFeng, H. Ganghui and W. Jing, "Self-adversarial variational autoencoder with spectral residual for time series anomaly detection," Neurocomputing, vol. 458, pp. 349-363, 2021.
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- T. Lee, Y. Kim, Y. Hyun, J. Mo and Y. Yoo, "Unsupervised Anomaly Detection Process Using LLE and HDBSCAN by Style-GAN as a Feature Extractor," International Journal of Precision Engineering and Manufacturing, vol. 25, 2023.
- Y. Zhou, H. Ren, Z. Li and W. Pedrycz, "An anomaly detection framework for time series data: An interval-based approach," Knowledge-Based Systems, vol. 228, 2021.
- L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich and . K.-R. Muller, "A Unifying Review of Deep and Shallow Anomaly Detection," Proceedings of the IEEE, vol. 109, no. 5, p. 756–795, 2021.
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- P. Jaccard, "Comparative study of the floral distribution in a portion of the Alps and Jura," Bulletin of the Vaudois Society of Natural Sciences, pp. 547-579, 1901.
- L. R. Dice, "Measures of the Amount of Ecologic Association Between Species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.
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References
P. K. Shabad, A. Alrashide and M. Osama, "Anomaly Detection in Smart Grids using Machine Learning," 2021.
S. S. Aljameel, D. M. Alomari, S. Alismail, F. Khawaher, A. A. Alkhudhair, F. Aljubran and R. M. Alzannan, "An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning," Computation, vol. 10, no. 8, 2022.
E. Swartling and P. Hanna, Anomaly Detection in Time Series Data using Unsupervised Machine Learning Methods: A Clustering-Based Approach, 2020.
S. Russo, M. Lürig, w. hao, B. Matthews and . K. Villez, "Active learning for anomaly detection in environmental data," Environmental Modelling and Software, vol. 134, 2020.
M. I. Radaideh, C. Pappas, J. Walden, D. Lu, L. Vidyaratne, T. Britton, K. Rajput, M. Schram and S. Cousineau, "Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders," Digital Signal Processing, vol. 130, 2022.
L. Yunxiao, L. Youfang, X. QinFeng, H. Ganghui and W. Jing, "Self-adversarial variational autoencoder with spectral residual for time series anomaly detection," Neurocomputing, vol. 458, pp. 349-363, 2021.
M. Van Onsem, D. De Paepe, . S. Vanden Hautte, P. Bonte, V. Ledoux, A. Lejon, F. Ongenae, D. Dreesen and S. Van Hoecke, "Hierarchical pattern matching for anomaly detection in time series," Computer Communications, vol. 193, pp. 75-81, 2022.
K. Chang, Y. Yoo and J.-G. Baek, "Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing," Sensors, vol. 21, no. 11, 2021.
T. Lee, Y. Kim, Y. Hyun, J. Mo and Y. Yoo, "Unsupervised Anomaly Detection Process Using LLE and HDBSCAN by Style-GAN as a Feature Extractor," International Journal of Precision Engineering and Manufacturing, vol. 25, 2023.
Y. Zhou, H. Ren, Z. Li and W. Pedrycz, "An anomaly detection framework for time series data: An interval-based approach," Knowledge-Based Systems, vol. 228, 2021.
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich and . K.-R. Muller, "A Unifying Review of Deep and Shallow Anomaly Detection," Proceedings of the IEEE, vol. 109, no. 5, p. 756–795, 2021.
D. Freedman and P. Diaconis, "On the histogram as a density estimator: L2 theory," Probability Theory and Related Fields, vol. 57, no. 4, pp. 453-476, 1981.
C.-C. M. Yeh, Y. Zhu, L. Ulanova, N. Begum, Y. Ding, H. A. Dau, D. F. Silva, A. Mueen and E. Keogh, "Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets," in 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016, pp. 1317-1322.
P. Jaccard, "Comparative study of the floral distribution in a portion of the Alps and Jura," Bulletin of the Vaudois Society of Natural Sciences, pp. 547-579, 1901.
L. R. Dice, "Measures of the Amount of Ecologic Association Between Species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.
"Scikit-learn," [Online]. Available: https://scikit-learn.org.