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2025 | OriginalPaper | Chapter

7. Information and Measuring Systems for Statistical Diagnostics of Electrical Equipment

Authors : Vitalii Babak, Sergii Babak, Artur Zaporozhets

Published in: Statistical Diagnostics of Electric Power Equipment

Publisher: Springer Nature Switzerland

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Abstract

This chapter explores the implementation and effectiveness of statistical diagnostics systems for monitoring and diagnosing electrical equipment (EE) components using information-measuring systems (IMSs). The chapter delves into the limitations of traditional diagnostic methods and how IMSs, through advanced data analysis and automation, overcome these limitations. By proposing a generalized structure for IMSs ‒ incorporating sensors, processing units, and software components, the chapter showcases specific applications such as diagnosing rolling bearings and various other EE units including internal combustion engines and electrical machines. It emphasizes the role of statistical hypothesis testing, linear random process models, and the critical use of training sets in constructing accurate diagnostic decision rules. Experimental examples, including shock and vibration diagnostics, illustrate the system's capabilities in real-world applications. Through detailed analysis of errors including methodological, instrumental, discretization, and quantization errors, the chapter presents calculation relations for metrological control, thereby enhancing diagnostic accuracy. This comprehensive overview confirms the potential of IMSs to significantly improve the diagnostic processes of EE, highlighting the importance of statistical methods, advanced data processing, and the strategic selection of diagnostic signals in reducing errors and improving reliability.

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Eremenko, V., Zaporozhets, A., Isaienko, V., Babikova, K.: Application of wavelet transform for determining diagnostic signs. In: Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Vol. 1, pp. 12–15 (2019)

Babak, V., Eremenko, V., Zaporozhets, A.: Research of diagnostic parameters of composite materials using Johnson distribution. Int. J. Comput. 18(4), 483–494 (2019)CrossRef

Zaporozhets, A., Eremenko, V., Isaenko, V., Babikova, K.: Approach for creating reference signals for detecting defects in diagnosing of composite materials. In: Conference on Computer Science and Information Technologies, pp. 154–172. Springer International Publishing, Cham (2019)

Lei, Y., Kong, D., Lin, J., Zuo, M.J.: Fault detection of planetary gearboxes using new diagnostic parameters. Meas. Sci. Technol. 23(5), 055605 (2012)CrossRef

Song, L., Wang, H., Chen, P.: Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery. IEEE Trans. Instrum. Meas. 67(8), 1887–1899 (2018)CrossRef

Sverdlova, A., Zaporozhets, A. Predicting anomaly conditions of energy equipment using neural networks. In: E3S Web of Conferences (Vol. 280, p. 09005) (2021)

Babak, V., Scherbak, L., Kuts, Y., Zaporozhets, A.: Information and measurement technologies for solving problems of energy informatics. In: The 1st International Workshop on Information Technologies: Theoretical and Applied Problems 2021. CEUR Workshop Proceedings, vol. 3039, pp. 24–31 (2021)

Das, U.K., Tey, K.S., Seyedmahmoudian, M., Mekhilef, S., Idris, M.Y.I., Van Deventer, W., Stojcevski, A.: Forecasting of photovoltaic power generation and model optimization: a review. Renew. Sustain. Energy Rev. 81, 912–928 (2018)CrossRef

Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.C., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Wirel. Commun. 24(2), 98–105 (2016)CrossRef

10.

Conejo, A.J., Carrión, M., Morales, J.M.: Decision making under uncertainty in electricity markets, vol. 1, pp. 376–384. Springer, New York (2010)

11.

Malik, H., Iqbal, A., Yadav, A.K.: Soft computing in condition monitoring and diagnostics of electrical and mechanical systems, vol. 1096, p. 499. Springer, Berlin/Heidelberg, Germany (2020)

12.

Henao, H., Capolino, G.A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Hedayati-Kia, S.: Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind. Electron. Mag. 8(2), 31–42 (2014)CrossRef

13.

Witczak, M.: Fault diagnosis and fault-tolerant control strategies for non-linear systems. vol. 266. Springer International Publishing, Heidelberg, Germany, pp. 375–392 (2014)

14.

Eremenko, V., Babak, V., Zaporozhets, A.: Method of reference signals creating in non-destructive testing based on low-speed impact. Tekhnichna Elektrodynamika 4, 070 (2021)CrossRef

15.

Zaporozhets, A., Babak, V., Sverdlova, A., Isaienko, V., Babikova, K.: Development of a system for diagnosing heat power equipment based on IEEE 802.11 s. In: Systems, Decision and Control in Energy II, pp. 141–151. Springer International Publishing, Cham (2021)

16.

Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Period. Polytech. Mech. Eng. 63(4), 241–248 (2018)CrossRef

17.

Karlaftis, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp. Res. Part C: Emerg. Technol. 19(3), 387–399 (2011)CrossRef

18.

Yin, S., Ding, S.X., Xie, X., Luo, H.: A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Industr. Electron. 61(11), 6418–6428 (2014)CrossRef

19.

Zaporozhets, A.O.: Correlation analysis between the components of energy balance and pollutant emissions. Water Air Soil Pollut. 232, 1–22 (2021)CrossRef

20.

Babak, V. P., Babak, S. V., Eremenko, V. S., Kuts, Y. V., Myslovych, M. V., Scherbak, L. M., Zaporozhets, A.O.: Models and measures for the diagnosis of electric power equipment. Models Measures in Measurements Monitoring, pp. 99–126 (2021)

21.

Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and hardware for diagnosing thermal power equipment based on smart grid technology. In: Advances in Intelligent Systems and Computing III: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2018, September 11–14, Lviv, Ukraine, pp. 476–489. Springer International Publishing (2019)

22.

Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Principles of construction of systems for diagnosing the energy equipment. Diagnostic Systems for Energy Equipments, pp. 1–22 (2020)

23.

Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Y.V., Myslovych, M.V., Scherbak, L.M., Zaporozhets, A.O.: Models and measures for standardless measurements of the composite materials characteristics. Models and Measures in Measurements Monitor. 157–190 (2021)

24.

Babak, V., Zaporozhets, A., Zvaritch, V., Scherbak, L., Myslovych, M., Kuts, Y.: Models and measures in theory and practice of manufacturing processes. IFAC-PapersOnLine 55(10), 1956–1961 (2022)CrossRef

25.

Babak, V., Zaporozhets, A., Kovtun, S., Kuts, Y., Fryz, M., Scherbak, L.: Information provision for monitoring the current state of electric power facilities. In: Advanced System Development Technologies I, pp. 283–309. Springer Nature Switzerland, Cham (2023)

26.

Babak, V., Zaporozhets, A., Kuts, Y., Myslovych, M., Fryz, M., Scherbak, L.: Models and characteristics of identification of noise stochastic signals of research objects. In: CEUR Workshop Proceedings, vol. 3309, pp. 349–362 (2022)

27.

Babak, V., Zaporozhets, A., Kuts, Y., Scherbak, L.: Some features of Hilbert transform and their use in energy informatics. The Probl General Energy 1–2 (2022)

28.

Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Methods and models for information data analysis. Diagnost. Syst. Energy Equipments 23–70 (2020)

29.

Zvaritch, V., Myslovych, M., Gyzhko, Y.: Application of linear random processes to construction of diagnostic system for power engineering equipment. In: IFIP International Conference on Advances in Production Management Systems. Springer International Publishing, Cham, pp. 617–622 (2021)

30.

Myslovych, M., Hyzhko, Y., Hutorova, M., Ostapchuk, L.: On peculiarities of development of information support for technical diagnostics multilevel systems of electrical equipment. Comput. Probl. Electr. Eng. 9(2), 34–41 (2019)CrossRef

31.

Babak, S., Myslovych, M., Sysak, R.: Computerized systems for remote monitoring and diagnostics of electric power facilities. Comput. Probl. Electr. Eng. 5(1), 1–4. (2015)

32.

Zaitsev, I., Levytskyi, A., Bereznychenko, V.:. Hybrid diagnostics systems for power generators faults: systems design principle and shaft run-out sensors. Power Systems Research and Operation: Selected Problems, 71–98 (2022)

33.

Šiniković, G., Gubeljak, N., Veg, E., Regodić, M., Jagarinec, D., Medojević, I., Jeli, Z.: Rolling bearing fault detection in the range of ultrasound. Tehnički vjesnik 30(3), 830–836 (2023)

34.

Mazal, P., Nohal, L., Komenda, L.: Diagnostics of damage development of bearings and gears by acoustic emission method. Int. J. Microstruct. Mater. Prop. 9(3–5), 433–447 (2014)

35.

Golonka, E., Pająk, M., Kolar, D.: Selected diagnostic methods used in Hydroelectric Power Plants. In: MATEC Web of Conferences, vol. 332, p. 01011. EDP Sciences (2021)

36.

Babak, V., Zaporozhets, A., Kovtun, S., Myslovych, M., Kuts, Y., Scherbak, L.: Information support for identification of the technical state of electric power facilities. In: Systems, Decision and Control in Energy V, pp. 129–153. Springer Nature Switzerland, Cham (2023)

37.

Kucera, L., Gajdosik, T., Bucala, J.: The Vibrodiagnostics of damaged gears of planetary gearboxes. Commun.-Sci. Lett. Univ. Zilina 16(3A), 67–73 (2014)

38.

Dybała, J.: Diagnosing of rolling-element bearings using amplitude level-based decomposition of machine vibration signal. Measurement 126, 143–155 (2018)CrossRef

39.

Mitrovic, R.M., Miskovic, Z.Z., Djukic, M.B., Bakic, G.M.: Statistical correlation between vibration characteristics, surface temperatures and service life of rolling bearings–artificially contaminated by open pit coal mine debris particles. Proced. Struct. Integrity 2, 2338–2346 (2016)CrossRef

40.

Atanasovska, I.D., Soldat, N.D., Patil, S.S., Mitrovic, R.M., Tomovic, R.Z.: Damage factor calculation for condition monitoring of rolling bearings. Arab. J. Sci. Eng. 48(3), 3181–3194 (2023)CrossRef

41.

Barszcz, T., Barszcz, T.: Standard vibration analysis methods. Vibration-Based Cond. Monitor. Wind Turbines, 33–86 (2019)

42.

Arhun, S., Migal, V., Hnatov, A., Ponikarovska, S., Hnatova, A., Novichonok, S.: Determining the quality of electric motors by vibro-diagnostic characteristics. EAI Endorsed Trans. Energy Web 7(29), e6–e6 (2020)

43.

Mansurov, T., Aliyev, C.: Development of an information-measuring system for measuring the dynamic parameters of moving objects. In: 2021 IEEE 8th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), pp. 23–27. IEEE (2021)

44.

Skliarov, V.: Nondestructive evaluation and monitoring of vibration characteristics of equipment by MEMS sensors. In: Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIV, vol. 11380, pp. 215–226 SPIE (2020)

45.

Zaitsev, I., Bereznychenko, V.: Condition monitoring and fault diagnosis systems of power generators with non-contact shaft runout electrocapacitive transducer. In: 2023 IEEE 4th KhPI Week on Advanced Technology (KhPIWeek) (pp. 1–6). IEEE (2023)

46.

Glowacz, A., Tadeusiewicz, R., Legutko, S., Caesarendra, W., Irfan, M., Liu, H., Xiang, J.: Fault diagnosis of angle grinders and electric impact drills using acoustic signals. Appl. Acoust. 179, 108070 (2021)CrossRef

47.

Zhang, L., Shang, H.C., Zhao, Z.Q., Zhang, D.F.: Evaluation of the dynamic characteristics of AZ91D crankcase covers and boxes. Strength Mater. 47, 128–135 (2015)CrossRef

48.

Addabbo, T., Fort, A., Landi, E., Moretti, R., Mugnaini, M., Parri, L., Vignoli, V.: A Characterization system for bearing condition monitoring sensors, a case study with a low power wireless Triaxial MEMS based sensor. In: 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, pp. 11–15. IEEE (2020)

49.

Soloiu, V., Weaver, A., Parker, L., Brant, A., Smith, R., III., Ilie, M., Carapia, C.: Constant volume combustion chamber (CVCC) investigations of aerospace F-24 and Jet-A in low-temperature heat release and negative temperature coefficient regions. Energy Convers. Manage. 263, 115687 (2022)CrossRef

50.

Landi, E., Spinelli, F., Intravaia, M., Mugnaini, M., Fort, A., Bianchini, M., Corradini, B.T., Scarselli, F., Tanfoni, M. (2023) A mobilenet neural network model for fault diagnosis in roller bearings. In: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 01–06. IEEE.

51.

Ftoutou, E., Chouchane, M.: Diesel engine injection faults’ detection and classification utilizing unsupervised fuzzy clustering techniques. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 233(16), 5622–5636 (2019)CrossRef

52.

Kovenburg, R., Slezak, A., George, C., Gale, R., Aksak, B.: The orientation dependence of the fingerprint effect for slip speed estimation and control. IEEE Sens. J. 23(5), 5437–5447 (2023)CrossRef

53.

Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Y.V., Myslovych, M.V., Scherbak, L.M., Zaporozhets, A.O.: Problems and features of measurements. Models Measures Measurements Monitor. pp. 1–31 (2021)

54.

Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Y.V., Myslovych, M.V., Scherbak, L.M., Zaporozhets, A.O.: Models of measuring signals and fields. Models Measures in Measurements and Monit. 33–59 (2021)

55.

Guo, L., Lei, Y., Xing, S., Yan, T., Li, N.: Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Trans. Industr. Electron. 66(9), 7316–7325 (2018)CrossRef

56.

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., Nandi, A.K.: Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech. Syst. Signal Process. 138, 106587 (2020)CrossRef

57.

Stone, G.C., Culbert, I., Boulter, E.A., & Dhirani, H.: Electrical insulation for rotating machines: design, evaluation, aging, testing, and repair, vol. 83. John Wiley & Sons (2014)

58.

Nguefack, L.T., Pauné, F., Mbihi, J.: Experimental study of ECG signal transmission system via a coaxial cable line using duty-cycle modulation. Algerian J. Eng. Technol. 4, 1–6 (2021)

59.

Jarutus, N., Kumsuwan, Y.: A unified duty-cycle modulation algorithm for a three-level NPC inverter. AEU-Int. J. Electron. Commun. 172, 154931 (2023)CrossRef

60.

Niu, F., Huang, X., Ge, L., Zhang, J., Wu, L., Wang, Y., Fang, Y.: A simple and practical duty cycle modulated direct torque control for permanent magnet synchronous motors. IEEE Trans. Power Electron. 34(2), 1572–1579 (2018)CrossRef

61.

Agoston, K.: Accelerometer characteristics, errors and signal conditioning. In: The International Conference Interdisciplinarity in Engineering INTER-ENG, p. 276. Elsevier Limited (2012)

62.

Gómez Pau, Á., Sanahuja Moliner, R., Balado Suárez, L.M., Figueras Pàmies, J.: Built-In test of MEMS capacitive accelerometers for field failures and aging degradation. In: Proceedings of XXVIIth conference on design of circuits and integrated systems, pp. 223–228 (2012)

63.

Hernandez, W., de Vicente, J., Sergiyenko, O., Tyrsa, V.: Estimation of the acceleration of a car under performance tests by using an optimal observer. In: IECON 2010–36th Annual Conference on IEEE Industrial Electronics Society, pp. 2834–2838. IEEE (2010)

64.

Dornfeld, D.: Application of acoustic emission techniques in manufacturing. NDT and E Int. 25(6), 259–269 (1992)CrossRef

65.

Babak, V., Bohachev, I., Zaporozhets, A., Khaidurov, V., Havrysh, V., Kalinichenko, A.: Some features of modeling ultrasound propagation in non-destructive control of metal structures based on the magnetostrictive effect. Electronics 12(3), 477 (2023)CrossRef

66.

Shcherban, A., Eremenko, V., Mokiichuk, V., Zaporozhets, A.: Model of information signals formation in the diagnostics of composite products. In: Advanced Information-Measuring Technologies and Systems I, pp. 209–223. Springer Nature Switzerland, Cham (2023)

67.

AlShorman, O., Alkahatni, F., Masadeh, M., Irfan, M., Glowacz, A., Althobiani, F., Glowacz, W.: Sounds and acoustic emission-based early fault diagnosis of induction motor: a review study. Adv. Mech. Eng. 13(2), 1687814021996915 (2021)CrossRef

68.

Shiroishi, J.Y.S.T., Li, Y., Liang, S., Kurfess, T., Danyluk, S.: Bearing condition diagnostics via vibration and acoustic emission measurements. Mech. Syst. Signal Process. 11(5), 693–705 (1997)CrossRef

69.

Nazarchuk, Z., Skalskyi, V., Serhiyenko, O.: Acoustic emission. Foundat. Eng. Mech. (2017)

70.

Zaporozhets, A., Eremenko, V., Babak, V., Isaienko, V., Babikova, K.: Using Hilbert transform in diagnostic of composite materials by impedance method. Period. Polytechnica Elect. Eng. Comput. Sci. 64(4), 334–342 (2020)CrossRef

71.

Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M. (2020). Technical provision of diagnostic systems. Diagnost. Syst. Energy Equipm. 91–133

72.

Lytvynenko, V., Lure, I., Boskin, O., Okipnyi, I.: Automation of acoustic-emission diagnostic systems control processes. Sci. J. TNTU 97(1), 88–96 (2020)

73.

Stakhova, A., Kvasnikov, V.: Structure construction of acoustic emission system for diagnostic of friction clusters. In: 2018 IEEE 13th international scientific and technical conference on computer sciences and information technologies (CSIT), vol. 1, pp. 64–67. IEEE (2018)

74.

Marasanov, V., Stepanchikov, D., Sharko, A., Sharko, A.: Operator of the dynamic process of the appearance of acoustic emission signals during deforming the structure of materials. In: 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO), pp. 646–650. IEEE (2020)

75.

Zaporozhets, A.O.: Experimental research of a computer system for the control of the fuel combustion process. Cont. Fuel Combust. Boilers, pp. 89–123 (2020)

76.

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: 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 48–51. IEEE (2018)

77.

Gasparjans, A., Terebkovs, A., Ziravecka, A.: Application of mathematical methods for current diagnostics of cylinder-piston group elements of diesel engines. In: Engineering for Rural Development. Proceedings of the International Scientific Conference (Latvia) (No. 19). Latvia University of Life Sciences and Technologies (2020)

78.

Hrynkiv, A., Aulin, V., Lysenko, S., Rogovskii, I., Titova, L., Zagurskiy, O., Kolosok, I.: Development of a system for determining the informativeness of the diagnosing parameters for a cylinder-piston group in the diesel engine during operation. Eastern-Eur J Enterprise Technol. 3(5–105), 19–29 (2020)CrossRef

79.

Kapłan, M., Klimek, K., Maj, G., Zhuravel, D., Bondar, A., Lemeshchenko-Lagoda, V., Wałowski, G.: Method of evaluation of materials wear of cylinder-piston group of diesel engines in the biodiesel fuel environment. Energies 15(9), 3416 (2022)CrossRef

80.

Wang, J., Li, H., Grinkevych, K., Huang, R., Xu, J., Tsybanov, G., Tkachenko, I.: Cyclic indentation method applied to evaluating surface degradation of cylinder-piston group parts. Strength Mater. 1–9 (2021)

81.

Gasparjans, A., Terebkovs, A., Zhiravetska, A.: Voltage spectral structure as a parameter of system technical diagnostics of ship diesel engine-synchronous generators. Electr. Control. Commun. Eng. 8(1), 37–42 (2015)CrossRef

82.

Gasparjan, A., Terebkov, A., Zhiravetska, A.: Monitoring of electro-mechanical system diesel-synchronous generator. In: 2015 IEEE 5th international conference on power engineering, energy and electrical drives (POWERENG), pp. 103–108. IEEE (2015)

83.

Morelos, M.A.M., Marín, J.A.: Fuzzy control strategy for stoichiometric air-fuel mixture in automotive systems. In: World Automation Congress 2012, pp. 1–6. IEEE (2012)

84.

Zhiravetska, A., Gasparjan, A., Terebkov, A.: Monitoring of current technical condotion of vessel diesel-generator installation. In: 2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe), pp. P-1. IEEE (2017)

85.

Xu, L., Wei, B., Yu, Y., Guerrero, J.M., Vasquez, J.: Coordinated control of diesel generators and batteries in dc hybrid electric shipboard power system. Energies 14(19), 6246 (2021)CrossRef

86.

Zaporozhets, A.O.: Methods and means for the control of the fuel combustion process. Cont. Fuel Combust. Boilers, 1–33 (2020)

87.

Zaporozhets, A.O.: Research of the process of fuel combustion in boilers. Cont. Fuel Combust. Boilers, 35–60 (2020)

88.

Mohammed, A., Pasupuleti, J., Khatib, T., Elmenreich, W.: A review of process and operational system control of hybrid photovoltaic/diesel generator systems. Renew. Sustain. Energy Rev. 44, 436–446 (2015)CrossRef

89.

Mobarra, M., Rezkallah, M., Ilinca, A.: Variable speed diesel generators: performance and characteristic comparison. Energies 15(2), 592 (2022)CrossRef

90.

Babak, S., Babak, V., Zaporozhets, A., Sverdlova, A.: Method of statistical spline functions for solving problems of data approximation and prediction of objects state. In: Proceedings of the Second International Workshop on Computer Modeling and Intelligent Systems (CMIS-2019), Zaporizhzhia, Ukraine, April, pp. 15–19 (2019)

91.

Babak, V., Zaporozhets, A., Kuts, Y., Scherbak, L., Eremenko, V.: Application of material measure in measurements: theoretical aspects. In: Systems, Decision and Control in Energy II, pp. 261–269. Springer International Publishing, Cham (2021)CrossRef

92.

Babak, V., Zaporozhets, A., Kulyk, M., Kuts, Y., Scherbak, L.: Application of discrete Hilbert transform to estimate the characteristics of cyclic signals: information provision. In: Systems, Decision and Control in Energy IV: Volume I. Modern Power Systems and Clean Energy, pp. 93–115. Springer Nature Switzerland, Cham (2023)

93.

Gertler, J.: Fault detection and diagnosis in engineering systems. CRC Press (2017)CrossRef

94.

Templin, J., Henson, R.A.: Diagnostic measurement: theory, methods, and applications. Guilford press (2010)

95.

Patton, R.J., Frank, P.M., Clark, R.N. (eds.): Issues of Fault Diagnosis for Dynamic Systems. Springer Science & Business Media (2013)

96.

Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Simulation and software for diagnostic systems. Diagnost. Syst. Energy Equipments 71–90 (2020)

97.

Afzal, W., Torkar, R., Feldt, R.: A systematic review of search-based testing for non-functional system properties. Inf. Softw. Technol. 51(6), 957–976 (2009)CrossRef

98.

Breivold, H.P., Crnkovic, I., Larsson, M.: A systematic review of software architecture evolution research. Inf. Softw. Technol. 54(1), 16–40 (2012)CrossRef

99.

Rai, A., Upadhyay, S.H.: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol. Int. 96, 289–306 (2016)CrossRef

100.

Dai, X., Gao, Z.: From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans. Industr. Inf. 9(4), 2226–2238 (2013)CrossRef

101.

Gangsar, P., Tiwari, R.: Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: a state-of-the-art review. Mech. Syst. Signal Process. 144, 106908 (2020)CrossRef

102.

Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process. 96, 1–15 (2014)CrossRef

103.

Wang, Y., Xiang, J., Markert, R., Liang, M.: Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications. Mech. Syst. Signal Process. 66, 679–698 (2016)CrossRef

104.

Tong, X., Feng, Y., Zhao, A.: A survey on Neyman-Pearson classification and suggestions for future research. Wiley Interdisciplinary Rev.: Comput. Statist. 8(2), 64–81 (2016)MathSciNetCrossRef

105.

Tang, L., Tian, H., Huang, H., Shi, S., Ji, Q.: A survey of mechanical fault diagnosis based on audio signal analysis. Measurement 113294 (2023)

106.

Kelter, R.: Bayesian and frequentist testing for differences between two groups with parametric and nonparametric two-sample tests. Wiley Interdiscipl. Rev. Comput. Stat. 13(6), e1523 (2021)MathSciNetCrossRef

107.

Shaikh, K.B., Jawarkar, N.P., Ahmed, V.: Machine diagnosis using acoustic analysis: A review. In: 2021 IEEE Conference on Norbert Wiener in the 21st Century (21CW), pp. 1–6). IEEE (2021)

108.

Kim, W., Katipamula, S.: A review of fault detection and diagnostics methods for building systems. Sci. Technol. Built Environ. 24(1), 3–21 (2018)CrossRef

109.

Tidriri, K., Chatti, N., Verron, S., Tiplica, T.: Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges. Annu. Rev. Control. 42, 63–81 (2016)CrossRef

110.

Bollen, K.A., Biemer, P.P., Karr, A.F., Tueller, S., Berzofsky, M.E.: Are survey weights needed? A review of diagnostic tests in regression analysis. Ann. Rev. Statist. Appl. 3, 375–392 (2016)CrossRef

111.

Chen, H., Jiang, B.: A review of fault detection and diagnosis for the traction system in high-speed trains. IEEE Trans. Intell. Transp. Syst. 21(2), 450–465 (2019)MathSciNetCrossRef

112.

Dormann, C.F., Schymanski, S.J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Singer, A.: Correlation and process in species distribution models: bridging a dichotomy. J. Biogeogr. 39(12), 2119–2131 (2012)CrossRef

113.

Hong, Y.H., Kim, H.K., Lee, H.S.: Reconstruction of dynamic displacement and velocity from measured accelerations using the variational statement of an inverse problem. J. Sound Vib. 329(23), 4980–5003 (2010)CrossRef

114.

Vedmitskyi, Y.G., Kukharchuk, V.V., Hraniak, V.F., Wójcik, W., Zhassandykyzy, M., Yesmakhanova, L.: New non-system physical quantities for vibration monitoring of transient processes at hydropower facilities, integral vibratory accelerations. Przeglad Elektrotechniczny 93(3), 69–72 (2017)

115.

Xia, H., Cao, Y.M., De Roeck, G.: Theoretical modeling and characteristic analysis of moving-train induced ground vibrations. J. Sound Vib. 329(7), 819–832 (2010)CrossRef

116.

Bohachev, I., Babak, V., Zaporozhets, A.: Novel small-aperture transducers based on magnetostrictive effect for diagnostic systems. Technical Electrodynamics 3, 69–78 (2022)

117.

Myklestad, N.O.: Fundamentals of vibration analysis. Courier Dover Publications (2018)

118.

Malaric, R.: Instrumentation and measurement in electrical engineering. Universal-Publishers (2011)

119.

Muhlheim, M.D., Wood, R.: Technical basis for evaluating software-related common-cause failures (No. ORNL/SR-2016/130). Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States) (2016)

120.

Torbert, R.B., Russell, C.T., Magnes, W., Ergun, R.E., Lindqvist, P.A., LeContel, O., Lappalainen, K.: The FIELDS instrument suite on MMS: scientific objectives, measurements, and data products. Space Sci. Rev. 199, 105–135 (2016)CrossRef

121.

Liptak, B.G., Piovoso, M.J., Shinskey, F.G., Eren, H., Totherow, G.K., Jamison, J.E., Morgan, D., Hertanu, H.I., Marszal, E.M., Berge, J., McQuillan, D.: Instrument Engineers’ Handbook, Volume Two: Process Control and Optimization. CRC press (2018)

122.

Grabowski, A.: Cauchy mean theorem. Formalized Math 22(2), 157–166 (2014)CrossRef

123.

Cheng, J.: On multivariate fractional taylor’s and cauchy’mean value theorem. J. Math. Study 52(1), 38–52 (2019)MathSciNetCrossRef

124.

Pertusa, J.F., Morante-Redolat, J.M.: Main steps in image processing and quantification: the analysis workflow. Comput. Opt. Microsc. Methods Protocols, 3–21 (2019)

125.

Manna, A.K., Moulick, H.N., Patra, J., Bera, K.: Image processing with sampling and noise filtration in image reconigation process. Image 3(7), 35–47 (2014)

126.

Manríquez, R.: Metric relations in the fuzzy right triangle. Mathematics 11(19), 4056 (2023)CrossRef

127.

Ueki, J.: Chebotarev links are stably generic. Bull. Lond. Math. Soc. 53(1), 82–91 (2021)MathSciNetCrossRef

128.

Niibo, H., Ueki, J.: Idèlic class field theory for 3-manifolds and very admissible links. Trans. Am. Math. Soc. 371(12), 8467–8488 (2019)CrossRef

129.

Niibo, H., Ueki, J.: A Hilbert reciprocity law on 3-manifolds. Res. Math. Sci. 10(1), 3 (2023)MathSciNetCrossRef

130.

Stolarsky, K.B.: From wythoff’s nim to chebyshev’s inequality. Am. Math. Mon. 98(10), 889–900 (1991)MathSciNetCrossRef

131.

Cohen, J.E.: Markov’s inequality and Chebyshev’s inequality for tail probabilities: a sharper image. Am. Stat. 69(1), 5–7 (2015)MathSciNetCrossRef

132.

Navarro, J.: A very simple proof of the multivariate Chebyshev’s inequality. Commun. Statist.-Theory Methods 45(12), 3458–3463 (2016)MathSciNetCrossRef

Title: Information and Measuring Systems for Statistical Diagnostics of Electrical Equipment
Authors: Vitalii Babak
Sergii Babak
Artur Zaporozhets
Publisher: Springer Nature Switzerland
Book: Statistical Diagnostics of Electric Power Equipment
Print ISBN: 978-3-031-76252-9

Electronic ISBN: 978-3-031-76253-6

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-3-031-76253-6_7