Visual Anomaly Detection in Production Plants

Alexander Maier, Tim Tack, Oliver Niggemann


This paper presents a novel method for visual anomaly detection in production plants. Since the complexity of the plants and the number of signals that have to be monitored by the operator grows, there is a need of tools to overcome the information overflow. The human is highly able to recognize irregularities in figures. More than 80% of the perceived information is captured visually. The approach proposed in this paper exploits this fact and subjects data to make the operator able to find anomalies in the displayed figures. In three steps the operator is lead from the visualization of the normal behavior over the anomaly detection and the localization of the faulty module to the anomalous signal.


  1. Alfred, I. (1985). The plane with parallel coordinates. The Visual Computer, 1:69-91.
  2. Bronstein, M. M., Bronstein, A. M., Kimmel, R., and Yavneh, I. (2006). Multigrid multidimensional scaling. Numerical Linear Algebra with Applications, 13(2-3):149-171.
  3. Cleveland, W. (1993). Visualizing Data. AT&T Bell Laboratories.
  4. Frey, C. W. (2008). Diagnosis and monitoring of complex industrial processes based on self-organizing maps and watershed transformations. In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.
  5. Huang, M. L., Liang, J., and Nguyen, Q. V. (2009). A visualization approach for frauds detection in financial market. In Proceedings of the 2009 13th International Conference Information Visualisation, IV 7809, pages 197-202, Washington, DC, USA. IEEE Computer Society.
  6. Jolliffe, I. T. (2002). Principal Component Analysis. Springer.
  7. Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1):1-8.
  8. Keim, D. A., Kohlhammer, J., Ellis, G., and Mansmann, F., editors (2010). Mastering The Information Age - Solving Problems with Visual Analytics. Eurographics.
  9. Niggemann, O., Stein, B., Vodenc?arevic, A., Maier, A., and Kleine B√ľning, H. (2012). Learning behavior models for hybrid timed systems. In Twenty-Sixth Conference on Artificial Intelligence (AAAI-12), Toronto, Ontario, Canada.
  10. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. IEEE Symposium on Visual Languages, page 336.
  11. Thomas, J. J. and Cook, K. A. (2006). A visual analytics agenda. IEEE Computer Graphics and Applications, 26:10-13.
  12. Tufte, E. (2001). The visual display of quantitative information. Graphics Press.
  13. Tufte, E. R. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press LLC.

Paper Citation

in Harvard Style

Maier A., Tack T. and Niggemann O. (2012). Visual Anomaly Detection in Production Plants . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 67-75. DOI: 10.5220/0004039600670075

in Bibtex Style

author={Alexander Maier and Tim Tack and Oliver Niggemann},
title={Visual Anomaly Detection in Production Plants},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},

in EndNote Style

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Visual Anomaly Detection in Production Plants
SN - 978-989-8565-21-1
AU - Maier A.
AU - Tack T.
AU - Niggemann O.
PY - 2012
SP - 67
EP - 75
DO - 10.5220/0004039600670075