Linear Temporal Logic (LTL) Based Monitoring of Smart Manufacturing Systems



Published Oct 18, 2015
Gerald Heddy Umer Huzaifa Peter Beling Yacov Haimes Jeremy Marvel Brian Weiss Amy LaViers


The vision of Smart Manufacturing Systems (SMS) includes collaborative robots that can adapt to a range of scenarios. This vision requires a classification of multiple system behaviors, or sequences of movement, that can achieve the same high-level tasks. Likewise, this vision presents unique challenges regarding the management of environmental variables in concert with discrete, logic-based programming. Overcoming these challenges requires targeted performance and health monitoring of both the logical controller and the physical components of the robotic system. Prognostics and health management (PHM) defines a field of techniques and methods that enable condition-monitoring, diagnostics, and prognostics of physical elements, functional processes, overall systems, etc. PHM is warranted in this effort given that the controller is vulnerable to program changes, which propagate in unexpected ways, logical runtime exceptions, sensor failure, and even bit rot. The physical component’s health is affected by the wear and tear experienced by machines constantly in motion. The controller’s source of faults is inherently discrete, while the latter occurs in a manner that builds up continuously over time. Such a disconnect poses unique challenges for PHM. This paper presents a robotic monitoring system that captures and resolves this disconnect. This effort leverages supervisory robotic control and model checking with linear temporal logic (LTL), presenting them as a novel monitoring system for PHM. This methodology has been demonstrated in a MATLAB-based simulator for an industry inspired use-case in the context of PHM. Future work will use the methodology to develop adaptive, intelligent con- trol strategies to evenly distribute wear on the joints of the robotic arms, maximizing the life of the system.

How to Cite

Heddy, G. ., Huzaifa, U. ., Beling, P. ., Haimes, Y. ., Marvel, J. ., Weiss, B. ., & LaViers, A. . (2015). Linear Temporal Logic (LTL) Based Monitoring of Smart Manufacturing Systems. Annual Conference of the PHM Society, 7(1).
Abstract 162 | PDF Downloads 95



PHM, manufacturing system, smart manufacturing, Linear Temporal Logic, Automaton, discrete, sensor failure, continuous

Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63(1), 135–149.

Al-Habaibeh, A., & Gindy, N. (2000). A new approach for systematic design of condition monitoring systems for milling processes. Journal of Materials Processing Technology, 107(1), 243–251.

Altintas, Y., Verl, A., Brecher, C., Uriarte, L., & Pritschow,G. (2011). Machine tool feed drives. CIRP Annals Manufacturing Technology, 60(2), 779–796. Anderson, A. (2011). Report to the president on ensuring American leadership in advanced manufacturing. Executive Office of the President.

Barajas, L. G., & Srinivasa, N. (2008). Real-time diagnosetics, prognostics and health management for large-scale manufacturing maintenance systems. In Asme 2008 international manufacturing science and engineering conference collocated with the 3rd jsme/asme international conference on materials and processing (pp. 85– 94).

Batzel, T. D., & Swanson, D. C. (2009). Prognostic health management of aircraft power generators. Aerospace and Electronic Systems, IEEE Transactions on, 45(2), 473–482.

Biehl, S., Staufenbiel, S., Recknagel, S., Denkena, B., & Bertram, O. (n.d.). Thin film sensors for condition monitoring in ball screw drives.

Borisov, O., Fletcher, S., Longstaff, A., & Myers, A. (2013). New low cost sensing head and taut wire method for automated straightness measurement of machine tool axes. Optics and lasers in engineering, 51(8), 978– 985.

Choo, B., Beling, P. A., LaViers, A. E., Marvel, J. A., & Weiss, B. A. (2015). Adaptive Multi-scale PHM for Robotic Assembly Processes. Annual Conference of the PHM Society(In review).

Clarke, E. M. M., Peled, D., & Grumberg, O. (1999). Model checking. MIT Press.

Datta, K., Jize, N., Maclise, D., & Goggin, D. (2004). An ivhm systems analysis & optimization process. In Aerospace conference, 2004. proceedings. 2004 IEEE (Vol. 6, pp. 3706–3716).

Gastin, P., & Oddoux, D. (2001, July). Fast LTL to Bu ̈chi automata translation. In G. Berry, H. Comon, & A. Finkel (Eds.), Proceedings of the 13th International Conference on Computer Aided Verification (CAV’01) (Vol. 2102, p. 53-65). Paris, France: Springer.

Haimes, Y. Y. (2005). Risk modeling, assessment, and management (Vol. 40). John Wiley & Sons.

Haimes, Y. Y. (2012). Systems-based guiding principles for risk modeling, planning, assessment, management, and communication. Risk Analysis, 32(9), 1451–1467.

Hofmeister, J., Wagoner, R., & Goodman, D. (2013). Prognostic health management (phm) of electrical systems using conditioned-based data for anomaly and prognostic reasoning. Chemical Engineering Transactions, 33, 991-996.

Holland, S., Barajas, L., Salman, M., & Zhang, Y. (2010). PHM for Automotive Manufacturing and Vehicle Applications. Annual Prognostics and Health Management Conference.
Hu, S. J., & Koren, Y. (1997). Stream-of-variation theory for automotive body assembly. CIRP Annals Manufacturing Technology, 46(1), 1–6.

Huzaifa, Umer and Marvel, Jeremy A. and LaViers, Amy E.(2015). Incorporating Continuous System Parameters in an LTL-based Monitoring Scheme. Unpublished. ISO. (2002). Condition monitoring and diagnostics of machines vibration condition monitoring part 1: General procedures (Tech. Rep. No. ISO 13373-1). International Organization for Standardization.

ISO. (2003). Condition monitoring and diagnostics of machines data processing, communication and presentation part 1: General guidelines (Tech. Rep. No. ISO 13374-1). International Organization for Standardization.

ISO. (2004). Condition monitoring and diagnostics of machines, prognostics part 1: General guidelines (Vol. ISO/IEC Directives Part 2; Tech. Rep. No. ISO13381- 1). International Organization for Standardization.
ISO. (2012). Condition monitoring and diagnostics of machines vocabulary (Tech. Rep. No. ISO 13372). International Organization for Standardization.

Jeong, K.-Y., & Phillips, D. T. (2001). Operational efficiency and effectiveness measurement. International Journal of Operations & Production Management, 21(11), 1404–1416.

LaViers, A., Chen, Y., Belta, C., & Egerstedt, M. (2011). Automatic sequencing of ballet poses. Robotics & Automation Magazine, IEEE, 18(3), 87–95.

Marvel, J. A. (2014). Collaborative robots: A gateway into factory automation. Retrieved from

Nakajima, S. (1988). Introduction to TPM: total productive maintenance. Productivity Press, Inc, P. O. Box 3007, Cambridge, Massachusetts 02140, USA, 1988. 129.

Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Man- ufacturing Technology, 50(1-4), 297–313.

Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of operations management, 21(2), 129–149.

Shen, T., Wan, F., Cui, W., & Song, B. (2010). Application of prognostic and health management technology on aircraft fuel system. In Prognostics and health management conference, 2010 (pp. 1–7).

Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for Prognostics and Health Management (PHM) Techniques within Manufacturing Operations. Annual Conference of the PHM Society.
Technical Research Papers

Most read articles by the same author(s)