Intelligent Configuration of Condition Monitoring Algorithms

Abstract:

The aim of this paper is to present the design of a condition monitoring tool, its use for the intelligent configuration of pattern recognition algorithms, for fault detection, and for diagnosis applications. The modular design and functionality of the tool will be introduced. The tool, developed and implemented by Fraunhofer IPK, can be used, in particular, to support the development process of algorithms for condition monitoring of wear-susceptible components in production systems. An example of the industrial application of the tool will be presented. This will include the implementation of configured algorithms using the tool on an embedded system using Raspberry Pi 2 and MEMS sensor. Finally, the evaluation of these algorithms on an axis test rig at different operating parameters will be presented.

• Periodical: Applied Mechanics and Materials (Volume 794)
• Pages: 355-362
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.794.355
• Online since: October 2015
• Authors: Abdelhakim Laghmouchi*, Eckhard Hohwieler, Claudio Geisert, Eckart Uhlmann
• Keywords: Algorithm, Classification, Monitoring

References

[1] M. Stockinger, Untersuchung von Methoden zur Zustandsüberwachung von Werkzeugmaschinen mit Kugelgewindetrieb, Aachen, (2011).

[2] E. Uhlmann, A. Laghmouchi, Entwicklungstool zur Konfiguration und Verknüpfung von Condition Monitoring-Algorithmen, ZWF 1-2, pp.23-27, (2015).

DOI: 10.3139/104.111281

[3] L. Gelman, R. Zimroz, J. Birkel, H. Leigh-Firbank, D. Simms, B. Waterland, G. Whitehurst, Adaptive Vibration Condition Monitoring Technology for Local Damage in Gearboxes. In: International Journal Non-Destruktive Testing and Condition Monitoring 2005, pp.461-464.

DOI: 10.1784/insi.2005.47.8.461

[4] S. Takata, F. Kirnura, F.J.A.M. v. Houten, E. Westkämper, Maintenance: Changing Role in Life Cycle Management. In: CIRP Annals – Manufacturing Technology. 53 (2004) 643-655.

DOI: 10.1016/s0007-8506(07)60033-x

[5] A. Kuhn, G. Schuh, B. Stahl, Nachhaltige Instandhaltung. Trends, Potenziale und Handlungsfelder nachhaltiger Instandhaltung, Frankfurt, (2006).

[6] W. Sui, S. Osman, W. Wang, An adaptive envelope spectrum technique for bearing fault detection 25 (2014) 9, URL: http: /iopscience. iop. org/0957-0233/25/9/095004 [11. 12. 2014].

DOI: 10.1088/0957-0233/25/9/095004

[7] G. D'Elia, Fault detection in rotating machines by vibration signal processing technique. Bologna, Italy, PhD Thesis, 2008, http: /diem1. ing. unibo. it/dottorato/meccanica/elia. pdf [17. 01. 2015].

[8] S. Goreczke, J. Strackeljan, Optimisation of time domain features for roller bearing fault diagnosis, the sixth International Conference on Condition Monitoring and Machinery Failure Prevention-CM2009, Dublin 22. -25. Juni 2009, pp.642-652.

[9] Z. Bin, G. George, E Marcos, E. Orchard, S. Abhinav, and J. George: Rolling Element Bearing Feature Extraction and Anomaly Detection Based on Vibration Monitoring, In: 16th Medi. Conference on Control and Automation, 2008 Ajaccio, France, 25. -27 June 2008, pp.1792-1797.

DOI: 10.1109/med.2008.4602112

[10] R.J. Kruse, C. Borgelt, F. Klawonn, C. Moewes, G. Ruß, and M. Steinbrecher, Computational Intelligence - Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze, Vieweg + Teubner, Wiesbaden, (2011).

DOI: 10.1007/978-3-658-10904-2

[11] N. Bissantz, J. Hagedorn, Data Mining (Datenmustererkennung). WIRTSCHAFTS-INFORMATIK 51 (2009) 139-144.

DOI: 10.1007/s11576-008-0108-z

[12] J. Han, M. Kamber, J. Pei, Data Mining. Amsterdam, Elsevier/Morgan Kaufmann, (2012).

[13] T.A. Runkler, Data Mining - Methoden und Algorithmen intelligenter Datenanalyse. Vieweg + Teubner, Wiesbaden, (2010).

DOI: 10.1007/978-3-8348-2171-3

[14] T. van Do, U.P. Chong, Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain. Journal of Mechanical Engineering 57, (2011) 09, pp.655-666.

DOI: 10.5545/sv-jme.2010.162

[15] K. Peyman, A. Makinezhad, Using PCA in Acoustic Emission Condition Monitoring to Detect Faults in an Automobile Engine. In Wien, 08. - 09 October, (2010).

[16] VDI 3832: 2013-01, (04. 2013) Measurement of structure-borne sound of rolling element bearing in machines and plants for evaluation of condition.

[17] E. Uhlmann, A. Laghmouchi, N. Raue, Configurable Condition Monitoring Methods for Industrial Product Service Systems, Product-Service Integration for Sustainable Solutions, Bochum, 2013, pp.347-358.

DOI: 10.1007/978-3-642-30820-8_30

[18] H. Hennrich, Aufbau eines kombinierten belastungs- und zustandsorientierten Diagnose- und Prognosesystems für Kugelgewindetriebe. Forschungsberichte aus dem wbk Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT), eds.: J., Fleischer, G. Lanza, V., Schulze, Aachen, Karlsruher Inst. für Technologie (KIT), (2013).

DOI: 10.25368/2021.55

[19] W.H. Klein, Zustandsüberwachung von Rollen-Profilschienenführungen und Kugelgewindetrieben, Aachen, (2011).

[20] M. Niedermayer, C. Hoherz, D. Reinhardt, H. Scholtz, Drahtlose Tiefendiagnose, Energieautarke für Condition-Monitoring-Anwendungen, wt Werkstatttechnik online 104 (2014) 502-504.

DOI: 10.37544/1436-4980-2014-7-8-502