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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References
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)
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)
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)
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)
Lu, X., Nagelkerke, M., van de Wiel, D., Fahland, D.: Discovering interacting artifacts from ERP systems. IEEE Trans. Serv. Comput. 8, 861–873 (2015)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection. ACM Comput. Surv. 41, 1–58 (2009)
Aggarwal, C.C.: Outlier Analysis. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47578-3
Ibidunmoye, O., Hernández-Rodriguez, F., Elmroth, E.: Performance anomaly detection and bottleneck identification. ACM Comput. Surv. 48, 1–35 (2015)
Mehrotra, K.G., Mohan, C.K., Huang, H.: Anomaly Detection Principles and Algorithms. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67526-8
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)
Tang, L.-A., et al.: Trustworthiness analysis of sensor data in cyber-physical systems. J. Comput. Syst. Sci. 79, 383–401 (2013)
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)
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_28
Lu, W., et al.: Unsupervised sequential outlier detection with deep architectures. IEEE Trans. Image Process. 26, 4321–4330 (2017)
Xiong, Y., Zuo, R.: Recognition of geochemical anomalies using a deep autoencoder network. Comput. Geosci. 86, 75–82 (2016)
Sebestyen, G., Hangan, A.: Anomaly detection techniques in cyber-physical systems. Acta Univ. Sapientiae Inform. 9, 116 (2017)
Qu, Y., et al.: Product-based neural networks for user response prediction over multi-field categorical data (2018)
Rodríguez, P., Bautista, M.A., Gonzàlez, J., Escalera, S.: Beyond one-hot encoding: lower dimensional target embedding. Image Vis. Comput. 75, 21–31 (2018)
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)
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)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)
Acknowledgements
This study takes place within the project ProDok 4.0, funded by the German Ministry of Education and Research (BMBF) within the framework of the Services 2010 action plan under funding no. 02K14A110. Executive steering committee is the Karlsruher Institut für Technologie - Karlsruhe Institute of Technology (KIT). Project partners are KUKA AG, ISRA VISION AG, dictaJet Ingenieurgesellschaft mbH and Hochschule Darmstadt - University of Applied Sciences. Glass inspection machine (Type FS5D) logs provided by ISRA VISION AG. All rights on example log data remains solely to ISRA VISION AG. Usage must be requested.
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Kaupp, L., Beez, U., Hülsmann, J., Humm, B.G. (2019). Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_5
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DOI: https://doi.org/10.1007/978-3-030-20257-6_5
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