Abstract
Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group.
We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system.
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References
Alippi, C., Roveri, M., Trovò, F.: A “Learning from Models” cognitive fault diagnosis system. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7553, pp. 305–313. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33266-1_38
Alippi, C., Roveri, M., Trovò, F.: A self-building and cluster-based cognitive fault diagnosis system for sensor networks. IEEE Trans. Neural Netw. Learn. Syst. 25(6), 1021–1032 (2014)
Byttner, S., Nowaczyk, S., Prytz, R., Rögnvaldsson, T.: A field test with self-organized modeling for knowledge discovery in a fleet of city buses. In: IEEE ICMA, pp. 896–901 (2013)
Chen, H., Tiňo, P., Rodan, A., Yao, X.: Learning in the model space for cognitive fault diagnosis. IEEE TNNLS 25(1), 124–136 (2014)
D’Silva, S.H.: Diagnostics based on the statistical correlation of sensors. Technical paper 2008-01-0129. Society of Automotive Engineers (SAE) (2008)
Fan, Y., Nowaczyk, S., Rögnvaldsson, T.: Evaluation of self-organized approach for predicting compressor faults in a city bus fleet. Procedia Comput. Sci. 53, 447–456 (2015)
Fan, Y., Nowaczyk, S., Rögnvaldsson, T.: Incorporating expert knowledge into a self-organized approach for predicting compressor faults in a city bus fleet. Frontiers in Artificial Intelligence and Applications, vol. 278, pp. 58–67 (2015)
Filev, D.P., Chinnam, R.B., Tseng, F., Baruah, P.: An industrial strength novelty detection framework for autonomous equipment monitoring and diagnostics. IEEE Trans. Ind. Inform. 6, 767–779 (2010)
Filev, D.P., Tseng, F.: Real time novelty detection modeling for machine health prognostics. In: North American Fuzzy Information Processing Society (2006)
Fogelstrom, K.A.: Air brake system characterization by self learning algorithm (2006)
Fogelstrom, K.A.: Prognostic and diagnostic system for air brakes (2007)
Gadd, H., Werner, S.: Fault detection in district heating substations. Appl. Energy 157, 51–59 (2015)
Hansson, J., Svensson, M., Rögnvaldsson, T., Byttner, S.: Remote diagnosis modelling (2013)
Kargupta, H., et al.: VEDAS: a mobile and distributed data stream mining system for real-time vehicle monitoring. In: Fourth International Conference on Data Mining (2004)
Kargupta, H., et al.: MineFleet: the vehicle data stream mining system for ubiquitous environments. In: May, M., Saitta, L. (eds.) Ubiquitous Knowledge Discovery. LNCS (LNAI), vol. 6202, pp. 235–254. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16392-0_14
Kargupta, H., Puttagunta, V., Klein, M., Sarkar, K.: On-board vehicle data stream monitoring using mine-fleet and fast resource constrained monitoring of correlation matrices. New Gener. Comput. 25, 5–32 (2007)
Lapira, E.R.: Fault detection in a network of similar machines using clustering approach. Ph.D. thesis, University of Cincinnati (2012)
Lapira, E.R., Al-Atat, H., Lee, J.: Turbine-to-turbine prognostics technique for wind farms (2011)
Quevedo, J., et al.: Combining learning in model space fault diagnosis with data validation/reconstruction: application to the Barcelona water network. Eng. Appl. Artif. Intell. 30, 18–29 (2014)
Rögnvaldsson, T., Nowaczyk, S., Byttner, S., Prytz, R., Svensson, M.: Self-monitoring for maintenance of vehicle fleets. Data Min. Knowl. Discov. 32(2), 344–384 (2018)
Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl.-Based Syst. 123, 163–173 (2017)
Vachkov, G.: Intelligent data analysis for performance evaluation and fault diagnosis in complex systems. In: IEEE ICFS, pp. 6322–6329 (2006)
Zhang, Y., Gantt Jr., G.W., Rychlinski, M.J., Edwards, R.M., Correia, J.J., Wolf, C.E.: Connected vehicle diagnostics and prognostics, concept, and initial practice. IEEE Trans. Reliab. 58, 286–294 (2009)
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Nowaczyk, S., Sant’Anna, A., Calikus, E., Fan, Y. (2018). Monitoring Equipment Operation Through Model and Event Discovery. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_6
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DOI: https://doi.org/10.1007/978-3-030-03496-2_6
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