Condition Based Maintenance (CBM) is a well-known concept and it has been demonstrated that it is the way ahead to prognostic maintenance for failure avoidance and for the reduction of maintenance cost.
This paper presents an application for Condition Based Maintenance, with a specific focus on State Detection, according to MIMOSA OSA-CBM reference architecture. The papers aims at presenting peculiarity of development of such a kind of solution when considering the use of Smart Sensors instead of traditional devices.
Indeed, breakthrough in CBM is expected from the development of ICT and embedded systems. This technology supply integrated chips implementing all the necessary circuitry to manage field data capture, data processing, local diagnosis, local feedback (where possible) and information transfer to the upper control levels. These so-called smart sensors exploit new technologies of micro sensors (MEMS, micro electro mechanical systems) and wireless communication together with the computing power of a microprocessor.
In particular, applications related to maintenance and human safety appear to be very promising due to the unstructured nature of these domains, where self-configuring networks of intelligent devices can better comply with an ever changing and partially unpredictable environment.
A test case is deployed on a typical manufacturing equipment: a robot. The objective of the test case presented by the paper is not to develop new diagnostic algorithms, but to implement some statistical analysis within a monitoring infrastructure built with Smart Sensors.
The case of analysis that the paper will present grounds on the use of wireless sensor devices for temperature measures gathered on the electric motors of the robot. Then, data are transmitted through a wireless network to a receiver unit that accomplishes also elaboration by using statistical methods and then, thanks to a web-service communication, results are made available to external requests and users.
An advisory is generated when something is out of the normal behaviour of the equipment. Finally, the user can check this information through the Human Machine Interface available via web-service.
How to Cite
smart sensor, state detection, test case
Bengtsson, M., (2003). Standardization issues in condition based maintenance. Condition Monitoring and Diagnostic Engineering Management - Proceedings of the 16th International Congress, August 27-29, 2003.
Fumagalli, L., Ierace, S., Macchi, M., & Cavalieri, S. (2010). Assessment of industrial applicability of Electric Signature Analysis as diagnostic and prognostic tool. Proceedings of ESREL 2010 annual conference, Rhodes, Greece.
Fumagalli, L., Tavola, G., Macchi, M., Garetti, M., Holgado Granados, M., Checcozzo, R., Rusinà, F., Lionetto, A., Abbate N. (2011). Maintenance Management tools within a Service Oriented Architecture: Proposal for practical implementation. Proceedings of XVI Summer School "F. Turco", Abano Terme (Padova), 14-16 September 2011, Italy.
Fumagalli, L., Pala, S., Macchi, M. (2014). A web service-based toolbox for machine diagnostics based on statistical analysis. Proceedings of The 3rd International Workshop and Congress on eMaintenance, 17-18 June 2014, Lulea, Sweden.
Garetti, M., Macchi, M., Terzi, S., & Fumagalli, L. (2007, October). Investigating the organizational business models of maintenance when adopting self diagnosing and self healing ICT systems in multi site contexts. Proceedings of the IFAC Conference on Cost Effective Automation in Networked Product Development and Manufacturing IFAC-CEA (Vol. 7).
HTML 4.01 Specification. W3C Recommendation 24 December 1999. http://www.w3.org/TR/html4/
IEEE standard 802.15.4d-2009
ISO 13374:2007 Condition monitoring and diagnostics of machines - Data processing, communication and presentation.
Jardine, A., Lin, D., & D. Banjevic (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20 (7), pp. 1483-1510.
Lee J., Abujamra R., Jardine A.K.S., Lin D., & Banjevic D. (2004). An integrated platform for diagnostics, prognostics and maintenance optimization. Proceedings of the IMS ’2004 International Conference on Advances in Maintenance and in Modeling, Simulation and Intelligent Monitoring of Degradations. Arles, France.
Lastra J. L. M., Delamer M. (2006). Semantic web services in factory automation: fundamental insights and research roadmap IEEE Transactions on Industrial Informatics. IEEE Transactions, 1 (2), 1-11.
Lobov A., Ubis Lopez F., Villasenor Herrera V., Puttonen J., Lastra J. L. M. (2009). Semantic Web Services Framework for Manufacturing Industries. International Conference on Robostics and Biomimetics. Bangkok, IEEE,. 2104-2108.
Macchi, M., Fumagalli, L., Garetti, M., Tavola, G. Checcozzo, R., Rusinà, F., Vidales Ramos, A., Jokinen, J., Popescu, C., Martinez Lastra, J. L., Karhumäki, O., & Vainio, M. (2011) A Use case analysis method for the implementation of service-oriented solutions for monitoring and diagnostics. COMADEM.
MESA - ‘SOA in Manufacturing Guidebook’, While Paper 27, MESA International, IBM Corporation and Capgemini, 21May 2008.
Keith Mobley, R., (2002). An introduction to predictive maintenance. Butterworth-Heinemann, 2 edition, ISBN 0750675314
MB851 user manual. UM0894. http://www.st.com/st-webui/static/active/en/resource/technical/document/user_manual/CD00262415.pdf
MIMOSA. OSA-CBM specification, version 3.3.1. http://www.mimosa.org/?q=resources/specs/osa-bm v331
Muller, A., Crespo Marquez, A. & Iung, B. (2008) On the concept of e-maintenance: Review and current research. Reliability Engineering and System Safety, pp. 1165–1187.
Tsunami development board for TAO-3530 user manual. http://www.technexion.com/images/datasheets/tsunami.pdf
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.