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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0

Authors : Lukas Kaupp, Ulrich Beez, Jens Hülsmann, Bernhard G. Humm

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

Industry is changing rapidly under industry 4.0. The manufacturing process and its cyber-physical systems (CPSs) produce large amounts of data with many relationships and dependencies in the data. Outlier detection and problem solving is difficult in such an environment. We present an unsupervised outlier detection method to find outliers in temporal spatial log data without domain-specific knowledge. Our method is evaluated with real-world unlabeled CPS log data extracted from a quality glass inspection machine used in production. As a measurement metric for success, we set reasonable outlier areas in cooperation with a domain expert. Using our proposed method, we were able to find all known outlier areas. In addition, we found outliers that were not previously known and have been verified as outliers by a domain expert ex post.

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Literature
1.
Fu, Y., Zhu, J., Gao, S.: CPS information security risk evaluation system based on Petri Net. In: DSC 2017, 2017 IEEE Second International Conference on Data Science in Cyberspace, Proceedings, 26–29 June 2017, Shenzhen, China, pp. 541–548. IEEE, Piscataway (2017)
2.
Harada, Y., Yamagata, Y., Mizuno, O., Choi, E.-H.: Log-based anomaly detection of CPS using a statistical method. In: 8th IEEE International Workshop on Empirical Software Engineering in Practice, IWESEP 2017, Proceedings, 13 March 2017, Tokyo, Japan, pp. 1–6. Conference Publishing Services, IEEE Computer Society, Los Alamitos, California, Washington, Tokyo (2017)
3.
Nguyen, H., Cai, C., Chen, F.: Automatic classification of traffic incident’s severity using machine learning approaches. IET Intell. Transp. Syst. 11, 615–623 (2017)CrossRef
4.
Hasani, Z.: Robust anomaly detection algorithms for real-time big data. Comparison of algorithms. In: Stojanović, R. (ed.) 2017 6th Mediterranean Conference on Embedded Computing (MECO). Including ECYPS 2017, Proceedings: Research Monograph, Bar, Montenegro, 11th–15th June 2017, pp. 1–6. IEEE, Piscataway (2017)
5.
Lu, X., Nagelkerke, M., van de Wiel, D., Fahland, D.: Discovering interacting artifacts from ERP systems. IEEE Trans. Serv. Comput. 8, 861–873 (2015)CrossRef
6.
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRef
7.
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection. ACM Comput. Surv. 41, 1–58 (2009)CrossRef
8.
9.
Ibidunmoye, O., Hernández-Rodriguez, F., Elmroth, E.: Performance anomaly detection and bottleneck identification. ACM Comput. Surv. 48, 1–35 (2015)CrossRef
10.
11.
Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: Chawla, N., Wang, W. (eds.) Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 90–98. Society for Industrial and Applied Mathematics, Philadelphia (2017)CrossRef
12.
Tang, L.-A., et al.: Trustworthiness analysis of sensor data in cyber-physical systems. J. Comput. Syst. Sci. 79, 383–401 (2013)MathSciNetCrossRef
13.
Protopapadakis, E., Voulodimos, A., Doulamis, A., Doulamis, N., Dres, D., Bimpas, M.: Stacked autoencoders for outlier detection in over-the-horizon radar signals. Comput. Intell. Neurosci. 2017, 5891417 (2017)CrossRef
14.
Nolle, T., Seeliger, A., Mühlhäuser, M.: Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: Calders, T., Ceci, M., Malerba, D. (eds.) Discovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings, pp. 442–456. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-46307-0_​28CrossRef
15.
Lu, W., et al.: Unsupervised sequential outlier detection with deep architectures. IEEE Trans. Image Process. 26, 4321–4330 (2017)MathSciNetCrossRef
16.
Xiong, Y., Zuo, R.: Recognition of geochemical anomalies using a deep autoencoder network. Comput. Geosci. 86, 75–82 (2016)CrossRef
17.
Sebestyen, G., Hangan, A.: Anomaly detection techniques in cyber-physical systems. Acta Univ. Sapientiae Inform. 9, 116 (2017)MATH
18.
Qu, Y., et al.: Product-based neural networks for user response prediction over multi-field categorical data (2018)CrossRef
19.
Rodríguez, P., Bautista, M.A., Gonzàlez, J., Escalera, S.: Beyond one-hot encoding: lower dimensional target embedding. Image Vis. Comput. 75, 21–31 (2018)CrossRef
20.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Society for Artificial Intelligence and Statistics (2010)
21.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, pp. 807–814. Omnipress, USA (2010)
22.
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)
Metadata
Title
Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0
Authors
Lukas Kaupp
Ulrich Beez
Jens Hülsmann
Bernhard G. Humm
Copyright Year
2019
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20257-6_5

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