DOI: https://doi.org/10.15587/1729-4061.2017.108454

Evaluation to determine the efficiency for the diagnosis search formation method of failures in automated systems

Olena Syrotkina, Mykhailo Alekseyev, Oleksii Aleksieiev

Abstract


This paper describes the results of work in the field of failure self-diagnostics for automated systems in real time to increase the efficiency of their operation. We describe the method developed of a diagnosis search formation space by applying to the Expert System Knowledge Base to diagnose failures in automated systems. The input data for the Expert Diagnostic System is a conflicting set of diagnostic codes generated by the automated system over the time interval ∆t during its operation. We proposed mathematical methods to work with a data structure “m-tuples based on ordinary sets of arbitrary cardinality n” to process the input data. We conducted a comparative analysis to estimate the execution time of algorithms for the diagnosis search formation space using sequential access to the Boolean of input data and using the method developed. The analysis showed that the application of the proposed method changes the functional dependency of the execution time estimation of the algorithm in accordance with the number of its input data n from exponential to cubic. The application of the method developed allows us to minimize the time needed to establish the diagnosis to real time. The method presented to diagnose automated systems allows creating methods and algorithms for automatic self-recovery of their operability after reversible failures in real time

Keywords


expert diagnostic system; failure diagnostics; data organization structure; estimation of algorithm execution time

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References


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Dzhekson, P. (2001). Vvedenie v ekspertnye sistemy. Moscow: Vil'yams, 624.

Sirotkina, E. I., Alekseev, M. A. (2013). Formirovanie ob'ektno-klassifikatsionnoy modeli diagnostiki raboty SCADA sistemy. Problemy nedropol'zovaniya. Sankt-Peterburg, 256–258.

Syrotkina, O. (2015). Formation of the classification space of the expert system knowledge base for SCADA failure diagnostics. Power Engineering, Control and Information Technologies in Geotechnical Systems, 179–184. doi: 10.1201/b18475-25

Ponomarev, V. F. (2005). Matematicheskaya logika. Kaliningrad: izd-vo KGTU, 201.

Syrotkina, O. (2015). The application of specialized data structures for SCADA diagnostics. Sistemnye tekhnologyi, 4, 72–81.


GOST Style Citations


Ponomarev, O. P. Naladka i ekspluatatsiya sredstv avtomatizatsyi. SCADA-sistemy. Promyshlennye shiny i interfeysy. Obshchie svedeniya o programmiruemyh logicheskih kontrollerah i odnoplatnyh komp'yuterah [Text]: ucheb. pos. / O. P. Ponomarev. – Kaliningrad: Izd-vo in-ta «KVSHU», 2006. – 80 p.

Shopin, A. G. Evolyutsiya SCADA i informatsionnyh sistem proizvodstva [Text] / A. G. Shopin, I. V. Zanin // Avtomatizatsiya v promyshlennosti. – 2012. – Issue 1. – P. 18–21.

Berry, B. SCADA Tutorial: A Fast Introduction to SCADA Fundamentals and Implementation [Text] / B. Berry. – DPS Telecom, USA, 2011. – 12 p.

Goryainov, A. N. Opredelenie effektivnosti sistem diagnostirovaniya v teorii transportnoy diagnostiki [Text] / A. N. Goryainov // Vestnik NTU «KhPI». – 2012. – Issue 1. – P. 64–70.

Charbonnier, S. Analysis of fault diagnosability from SCADA alarms signatures using relevance indices [Text] / S. Charbonnier, N. Bouchair, P. Gayet // 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). – 2014. doi: 10.1109/smc.2014.6974342 

Munoz, M. A tool for the performance evaluation and failure detection of Amareleja PV plant (ACCIONA) from SCADA [Text] / M. Munoz, I. de la Parra, M. Garcia, J. Marcos, M. Perez // 2015 17th European Conference on Power Electronics and Applications (EPE'15 ECCE-Europe). – 2015. doi: 10.1109/epe.2015.7311722 

Wang, X. Research on Transformer Fault Diagnosis based on Multi-source Information Fusion [Text] / X. Wang, K. Wu, Y. Xu // International Journal of Control and Automation. – 2014. – Vol. 7, Issue 2. – P. 197–208. doi: 10.14257/ijca.2014.7.2.19 

Wang, K.-S. SCADA data based condition monitoring of wind turbines [Text] / K.-S. Wang, V. S. Sharma, Z.-Y. Zhang // Advances in Manufacturing. – 2014. – Vol. 2, Issue 1. – P. 61–69. doi: 10.1007/s40436-014-0067-0 

Windmann, S. Efficient fault detection for industrial automation processes with observable process variables [Text] / S. Windmann, O. Niggemann // 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). – 2015. doi: 10.1109/indin.2015.7281721 

MacGregor, J. Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods [Text] / J. MacGregor, A. Cinar // Computers & Chemical Engineering. – 2012. – Vol. 47. – P. 111–120. doi: 10.1016/j.compchemeng.2012.06.017 

Dzhekson, P. Vvedenie v ekspertnye sistemy [Text]: ucheb. pos. / P. Dzhekson. – Moscow: Vil'yams, 2001. – 624 p.

Sirotkina, E. I. Formirovanie ob'ektno-klassifikatsionnoy modeli diagnostiki raboty SCADA sistemy [Text] / E. I. Sirotkina, M. A. Alekseev // Problemy nedropol'zovaniya. – Sankt-Peterburg, 2013. – P. 256–258.

Syrotkina, O. Formation of the classification space of the expert system knowledge base for SCADA failure diagnostics [Text] / O. Syrotkina // Power Engineering, Control and Information Technologies in Geotechnical Systems. – 2015. – P. 179–184. doi: 10.1201/b18475-25 

Ponomarev, V. F. Matematicheskaya logika [Text]: ucheb. pos. / V. F. Ponomarev. – 2-e izd., ispr. i dop. – Kaliningrad: izd-vo KGTU, 2005. – 201 p.

Syrotkina, O. The application of specialized data structures for SCADA diagnostics [Text] / O. Syrotkina // Sistemnye tekhnologyi. – 2015. – Issue 4. – P. 72–81.



 

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Copyright (c) 2017 Olena Syrotkina, Mykhailo Alekseyev, Oleksii Aleksieiev

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061