HUGO: real-time analysis of component interactions in high-tech manufacturing equipment (industry article)

ABSTRACT

One of the major problems faced by the high-tech manufacturing industry is the need for automated and timely detection of anomalies which can lead to failures of the manufacturing equipment. Failures of the high-tech manufacturing equipment have a direct negative impact on the operating margin and consequently profit of the high-tech manufacturing industry. Automated and timely detection of anomalies is a difficult problem, the major challenge being the need to understand the interactions between large amount of machine components. Even very experienced system engineers are not aware of all interactions, especially if those need to be derived from high velocity sensor data. This, in turn, makes it impossible to recognize early warning signals and take action before failure happens.

In this paper we present HUGO -- a system for real-time analysis of component interactions in high-tech manufacturing equipment. HUGO automatically discovers (based on the available sensor data) correlations between machine components and helps engineers analyze them in real-time so as to be able to detect deterioration of the manufacturing equipment conditions in a timely fashion.

References

1. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for projected clustering of high dimensional data streams. In Proceedings of the Thirtieth international conference on Very large data bases - Volume 30, VLDB '04, pages 852--863. VLDB Endowment, 2004.
2. A. M. Agogino, S. Srinivas, and K. M. Schneider. Multiple sensor expert system for diagnostic reasoning, monitoring and control of mechanical systems. Mechanical Systems and Signal Processing, 2(2):165 -- 185, 1988.
3. R. Du, M. A. Elbestawi, and S. M. Wu. Automated monitoring of manufacturing processes, part 1: Monitoring methods. Journal of Engineering for Indunstry, 117:121 -- 132, 1995.
4. A. C. B. Garcia, C. Bentes, R. H. C. de Melo, B. Zadrozny, and T. J. P. Penna. Sensor data analysis for equipment monitoring. Knowl. Inf. Syst., 28(2):333--364, Aug. 2011.
5. A. K. Jardine, D. Lin, and D. Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7):1483 -- 1510, 2006.
6. Z. Jerzak, T. Heinze, M. Fehr, D. Gröber, R. Hartung, and N. Stojanovic. The debs 2012 grand challenge. In Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, DEBS '12, pages 393--398, New York, NY, USA, 2012. ACM.
7. D. C. Luckham. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2001.
8. I. Ntoutsi, A. Zimek, T. Palpanas, P. Kröger, and H.-P. Kriegel. Density-based projected clustering over high dimensional data streams. In SDM, pages 987--998, 2012.
9. S. J. Qin, G. Cherry, R. Good, J. Wang, and C. A. Harrison. Semiconductor manufacturing process control and monitoring: A fab-wide framework. Journal of Process Control, 16(3):179 -- 191, 2006.

Index Terms

• Applied computing

  • Enterprise computing

  • Operations research

    • Industry and manufacturing

• Hardware

  • Communication hardware, interfaces and storage

    • Signal processing systems