Abstract
The generalized principles of building information-measuring systems (IMS) designed to measure diagnostic signals of different physical nature (vibrational, acoustic, acoustic emission, thermal, electrical, etc.) that arise in operating electric power equipment are considered. The main diagnostic parameters that can be used as diagnostic features to determine the technical condition of various units of electric power equipment are analyzed. The main components that form the information support of the IMS of diagnostics of electric power equipment are considered.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Babak, S.V., Myslovych, M.V., Sysak, R.M.: Statistical diagnostics of electrical equipment (2015). ISBN 978-966-02-7704-5
Babak, V.P.: Hardware-software for monitoring the objects of generation, transportation and consumption of thermal energy (2016). ISBN 978-966-02-7967-4
Zaporozhets, A.A., Eremenko, V.S., Serhiienko, R.V., Ivanov, S.A.: Development of an intelligent system for diagnosing the technical condition of the heat power equipment. In: IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), pp. 48–51 (2018). https://doi.org/10.1109/stc-csit.2018.8526742
Zaporozhets, A., Eremenko, V., Isaenko, V., Babikova, K.: Approach for creating reference signals for detecting defects in diagnosing of composite materials. In: Shakhovska, N., Medykovskyy, M. (eds.) Advances in Intelligent Systems and Computing IV. Springer, Cham, vol. 1080, pp. 154–172 (2020). https://doi.org/10.1007/978-3-030-33695-0_12
Czichos, H.: Handbook of Technical Diagnostics. Fundamentals and Application to Structures and Systems (2013). ISBN 978-3-642-25850-3
Babak, V.P.: Information support for monitoring of thermal power facilities (2015). ISBN 978-966-02-7478-5
Stognii, B., Kyrylenko, O., Butkevych, O., Sopel, M.: Information support of problems of electric power systems control. Energy Econ. Technol. Ecol. 1(30), 13–22 (2012)
Edwards, S., Lees, A.W., Friswell, M.I.: Fault diagnosis of rotating machinery. Shock. Vib. Dig. 1(30), 4–13 (1998)
Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and hardware for diagnosing thermal power equipment based on smart grid technology. In: Shakhovska, N., Medykovskyy, M. (eds.) Advances in Intelligent Systems and Computing III. Springer, Cham, vol. 871, pp. 476–489 (2019). https://doi.org/10.1007/978-3-030-01069-0_34
Zaporozhets, A.: analysis of control system of fuel combustion in boilers with oxygen sensor. Period. Polytech. Mech. Eng. 64(4), 241–248 (2019). https://doi.org/10.3311/PPme.12572
Napolitano, A.: Generalizations of Cyclostationary Signal Processing: Spectral Analysis and Applications (2012). ISBN 9781119973355
Yatsuk, V., Mykyizhuk, M., Bubela, T.: Ensuring the measurement efficiency in dispersed measuring systems for energy objects. In: Królczyk, G., Wzorek, M., Król, A., Kochan, O., Su, J., Kacprzyk, J. (eds.) Sustainable Production: Novel Trends in Energy, Environment and Material Systems. Studies in Systems, Decision and Control, vol. 198, pp. 131–149 (2020). https://doi.org/10.1007/978-3-030-11274-5_9
Chen, P., Taniguchi, M., Toyota, T., He, Z.: Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming. Mech. Syst. Signal Process. 1(19), 175–194 (2005). https://doi.org/10.1016/j.ymssp.2003.11.004
Brie, D.: Modelling of the spalled rolling element bearing vibration signal: an overview and some new results. Mech. Syst. Signal Process. 3(14), 353–369 (2000). https://doi.org/10.1006/mssp.1999.1237
McCormick, A.C., Nandi, A.K.: Cyclostationarity in rotating machine vibrations. Mech. Syst. Signal Process. 2(12), 225–242 (1998). https://doi.org/10.1006/mssp.1997.0148
Williams Jr., J.H., DeLonga, D.M., Lee, S.S.: Correlations of acoustic emission with fracture mechanics parameters in structural bridge steels during fatigue. Mat. Eval. 40(10), 1184–1189 (1982)
Krishnakumari, A., Elayaperumal, A., Saravanan, M., Arvindan, C.: Fault diagnostics of spur gear using decision tree and fuzzy classifier. Int. J. Adv. Manuf. Technol. 9–12(89), 3487–3494 (2017). https://doi.org/10.1007/s00170-016-9307-8
Babak, S., Babak, V., Zaporozhets, A., Sverdlova, A.: Method of statistical spline functions for solving problems of data approximation and prediction of object state. In: CEUR Workshop Proceedings, vol. 2353, pp. 810–821 (2019) (Online). http://ceur-ws.org/Vol-2353/paper64.pdf
Domanski, P.D.: Statistical measures. In: Control Performance Assessment: Theoretical Analyses and Industrial Practice. Studies in Systems, Decision and Control, vol. 245, pp. 53–74 (2020). https://doi.org/10.1007/978-3-030-23593-2_4
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M. (2020). Principles of Construction of Systems for Diagnosing the Energy Equipment. In: Diagnostic Systems For Energy Equipments. Studies in Systems, Decision and Control, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-44443-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-44443-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44442-6
Online ISBN: 978-3-030-44443-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)