An effective data reduction model for machine emergency state detection from big data tree topology structures
References
- Abdi, H. and Williams, L.J. (2010). Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics 2(4): 433–459.
- Bansal, K. and Bansal, M. (2016). Data clustering and visualization based various machine learning techniques, International Journal of Advanced Research in Computer Science 7(6): 124–128.
- Bartenhagen, C., Klein, H.-U., Ruckert, C., Jiang, X. and Dugas, M. (2010). Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data, BMC Bioinformatics 11(1): 1–11.
- Basora, L., Olive, X. and Dubot, T. (2019). Recent advances in anomaly detection methods applied to aviation, Aerospace 6(11): 117.
- Cooper, C., Wang, P., Zhang, J., Gao, R.X., Roney, T., Ragai, I. and Shaffer, D. (2020a). Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals, Procedia Manufacturing 49: 105–111.
- Cooper, C., Zhang, J., Gao, R.X., Wang, P. and Ragai, I. (2020b). Anomaly detection in milling tools using acoustic signals and generative adversarial networks, Procedia Manufacturing 48: 372–378.
- Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, KDD’96 Proceedings, Portland, USA, pp. 226–231.
- Filter, L.V. and Filter, P. (2014). Seven techniques for dimensionality reduction, Technical report, KNIME, Zurich, https://www.knime.com/sites/default/files/inline-images/knime_seventechniquesdatadimreduction.pdf.
- Goldin, D.Q. and Kanellakis, P.C. (1995). On similarity queries for time-series data: Constraint specification and implementation, International Conference on Principles and Practice of Constraint Programming, Cassis, France, pp. 137–153.
- Hankerson, D., Hoffman, D., Leonard, D. and Linder, C. (2000). Coding Theory and Cryptography: The Essentials, CRC Press, Boca Raton.
- Hyndman, R.J., Wang, E. and Laptev, N. (2015). Large-scale unusual time series detection, 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, USA, pp. 1616–1619.
- Kamat, P. and Sugandhi, R. (2020). Anomaly detection for predictive maintenance in industry 4.0-a survey, E3S Web of Conferences 170: 02007.
- Liang, Y., Wang, S., Li, W. and Lu, X. (2019). Data-driven anomaly diagnosis for machining processes, Engineering 5(4): 646–652.
- Mühlbauer, M., Würschinger, H., Polzer, D., Ju, S. and Hanenkamp, N. (2020). Automated data labeling and anomaly detection using airborne sound analysis, Procedia CIRP 93: 1247–1252.
- Pathria, R. and Beale, P.D. (2011). Statistical Mechanics, Academic Press/Elsevier, Cambridge.
- Schleinkofer, U., Klöpfer, K., Schneider, M. and Bauernhansl, T. (2019). Cyber-physical systems as part of frugal manufacturing systems, Procedia CIRP 81: 264–269.
- Siboni, S. and Cohen, A. (2020). Anomaly detection for individual sequences with applications in identifying malicious tools, Entropy 22(6): 649.
- ur Rehman, M.H., Liew, C.S., Abbas, A., Jayaraman, P.P., Wah, T.Y. and Khan, S.U. (2016). Big data reduction methods: A survey, Data Science and Engineering 1(4): 265–284.
- Yao, Y.-C., Chen, Y.-H., Liu, C.-H. and Shih, W.-P. (2019). Real-time chatter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and local outlier factor algorithm, International Journal of Advanced Manufacturing Technology 103(1): 297–309.
- Żabiński, T., Mączka, T., Kluska, J., Madera, M. and Sęp, J. (2019). Condition monitoring in industry 4.0 production systems—The idea of computational intelligence methods application, Procedia CIRP 79: 63–67.
- Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H. and Chawla, N.V. (2019a). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data, Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, USA, pp. 1409–1416.
- Zhang, L., Elghazoly, S. and Tweedie, B. (2019b). Introducing AnomDB: An unsupervised anomaly detection method for CNC machine control data, Annual Conference of the Prognostics and Health Management Society, Scottsdale, USA.
Language: English
Page range: 601 - 611
Submitted on: Jun 11, 2021
Accepted on: Oct 19, 2021
Published on: Dec 30, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2021 Iaroslav Iaremko, Roman Senkerik, Roman Jasek, Petr Lukastik, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.