Browse > Article
http://dx.doi.org/10.9728/dcs.2018.19.4.789

LSTM-based Anomaly Detection on Big Data for Smart Factory Monitoring  

Nguyen, Van Quan (School of Electronics and Computer Engineering, Chonnam National University)
Van Ma, Linh (School of Electronics and Computer Engineering, Chonnam National University)
Kim, Jinsul (School of Electronics and Computer Engineering, Chonnam National University)
Publication Information
Journal of Digital Contents Society / v.19, no.4, 2018 , pp. 789-799 More about this Journal
Abstract
This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.
Keywords
Long Short-Term Memory (LSTM); Anomaly Detection; SCADA;
Citations & Related Records
연도 인용수 순위
  • Reference
1 L. Bontemps, J. McDermott, N.-A. Le-Khac, and others, "Collective anomaly detection based on long short-term memory recurrent neural networks," in International Conference on Future Data and Security Engineering, pp. 141-152, 2016.
2 S. D. Anton, D. Fraunholz, C. Lipps, F. Pohl, M. Zimmermann, and H. D. Schotten, "Two decades of SCADA exploitation: A brief history," in Application, Information and Network Security (AINS), 2017 IEEE Conference, pp. 98-104, 2017.
3 Understanding LSTM Network [Internet]. Available: http://colah.github.io/posts/2015-08-Understanding-LSTMs
4 Xisom solution: http://www.xisom.com
5 B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, "Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges," IEEE Access, Vol. 6, pp. 6505-6519, 2017.
6 T. Skripcak and P. Tanuska, "Utilisation of on-line machine learning for SCADA system alarms forecasting," Proceedings of 2013 Science and Information Conference, SAI 2013, pp. 477-484, 2013.
7 S. Mohanty, M. Jagadeesh, and H. Srivatsa, " 'Big Data' warehouse, 'BI'implementations and analytics," Apress, 2013.
8 I. Garitano, R. Uribeetxeberria and U. Zurutuza, "A review of SCADA Anomaly Detection System," Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, Vol. 87, 2011.
9 M. Shafiee and M. Finkelstein, "An optimal age-based group maintenance policy for multi-unit degrading systems," Reliability Engineering \& System Safety, Vol. 134, pp. 230-238, 2015.   DOI
10 E. Marchi, F. Vesperini, F. Weninger, F. Eyben, S. Squartini, and B. Schuller, "Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection," in Neural Networks (IJCNN), 2015 International Joint Conference, pp. 1-7, 2015.
11 Kv, R. Satish, and N. P. Kavya, "Trend Analysis of E-Commerce Data using Hadoop Ecosystem," International Journal of Computer Applications, Vol. 147, No. 6, pp. 1-5, 2016.
12 J. Dean and S. Ghemawat, "MapReduce: simplified data processing on large clusters," Communication of ACM, Vol. 51, No. 1, pp. 107-113, 2008.   DOI
13 Flume: http://flume.apache.org/
14 Hive:https://hive.apache.org/
15 Zeppelin: https://zeppelin.apache.org/docs/0.6.2/
16 L. Zhou, S. Pan, J. Wang, and A. V. Vasilakos, "Machine learning on big data: Opportunities and challenges," Neurocomputing, Vol. 237, pp. 350-361, 2017.   DOI
17 S. Hochreiter and J. Urgen Schmidhuber, "Long Short-Term Memory," Neural Computation, Vol. 9, No. 8, pp. 1735-1780, 1997.   DOI
18 Jordan, I. Michael and M. M. Tom, "Machine learning: Trends, perspectives, and prospects," Science , Vol.349, No. 6245, pp. 255-260, 2015.   DOI
19 V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM Computing survey (CSUR), Vol. 41, No. 3, pp. 1-58, 2009.
20 P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, "Long short term memory networks for anomaly detection in time series," Proceedings, pp. 89, 2015.
21 T. Olsson and A. Holst, "A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications," in FLAIRS Conference, pp. 434-439, 2015.