Top

Published in:

2025 | OriginalPaper | Chapter

8. Experimental Studies of Statistical Diagnostics Information and Measuring Systems

Authors : Vitalii Babak, Sergii Babak, Artur Zaporozhets

Published in: Statistical Diagnostics of Electric Power Equipment

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This comprehensive research chapter delves into the application of innovative diagnostic methods for electrical equipment (EE), focusing on vibration and acoustic analyses to detect and diagnose faults in rolling bearings of electric motors (EM) and the technical condition of diesel-electric generators (DEGs). Through extensive experimental setups, the chapter validates probabilistic models for diagnostic signals by utilizing real-world EE units and bespoke test benches. It introduces a specialized installation for analyzing rolling bearing vibrations, demonstrating the significance of frequency characteristics and the impact hammer test in pinpointing natural frequencies crucial for diagnostics. Furthermore, the study explores the use of shock diagnostics and acoustic emission (AE) analysis for evaluating the mechanical loading of EE components, highlighting the effectiveness of vibration maxima, and AE signal coefficients as reliable diagnostic indicators. Employing statistical hypothesis testing, particularly the Neyman-Pearson procedure, the research identifies specific faults in DEG cylinder-piston groups, showcasing the method's potential in early fault detection and preventive maintenance to enhance engine reliability. The findings contribute to the advancement of diagnostic methodologies for EE, promising improved maintenance strategies and operational efficiency.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Information and Measuring Systems for Statistical Diagnostics of Electrical Equipment

next chapter Tasks for Creating the Environmental Monitoring Systems for Energy Objects

Immovilli, F., Bellini, A., Rubini, R., Tassoni, C.: Diagnosis of bearing faults in induction machines by vibration or current signals: a critical comparison. IEEE Trans. Ind. Appl. 46(4), 1350–1359 (2010)CrossRef

Melnyk, O., Onyshchenko, S., Onishchenko, O., Lohinov, O., Ocheretna, V.: Integral approach to vulnerability assessment of ship’s critical equipment and systems. Trans. Maritime Sci. 12(01), 3–3 (2023)CrossRef

Burda, E.A., Zusman, G.V., Kudryavtseva, I.S., Naumenko, A.P.: An overview of vibration analysis techniques for the fault diagnostics of rolling bearings in machinery. In: Shock and Vibration (2022)

Abboud, D., Elbadaoui, M., Smith, W.A., Randall, R.B.: Advanced bearing diagnostics: a comparative study of two powerful approaches. Mech. Syst. Signal Process. 114, 604–627 (2019)CrossRef

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. In: Models and Measures in Measurements and Monitoring, pp. 99–126 (2021)

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)

Wrzochal, M., Adamczak, S., Piotrowicz, G., Wnuk, S.: Industrial experimental research as a contribution to the development of an experimental model of rolling bearing vibrations. Strojniški vestnik-J. Mechan. Eng. 68(9), 552–559 (2022)CrossRef

Adamczak, S., Stępień, K., Wrzochal, M.: Comparative study of measurement systems used to evaluate vibrations of rolling bearings. Procedia Eng. 192, 971–975 (2017)CrossRef

Rauber, T.W., da Silva Loca, A.L., de Assis Boldt, F., Rodrigues, A.L., Varejão, F.M.: An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals. Expert Syst. Appl. 167, 114022 (2021)CrossRef

10.

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

11.

Perales, N., Galar, D.: Inspection and Analysis of the Functioning of the Bearings Used on Railways: A Study of the Life of a Bearing Under Real Operating Conditions. Luleå Tekniska Universitet (2014)

12.

Dhanola, A., Garg, H.C.: Tribological challenges and advancements in wind turbine bearings: a review. Eng. Fail. Anal. 118, 104885 (2020)CrossRef

13.

Chen, L., Bu, D.T., Feng, Z.B., Liu, H.B.: Fatigue life analysis of remanufactured radial rolling bearing with the replaced loading zone. J. Eng. (2023)

14.

Liu, G., Cong, J., Wang, P., Du, S., Wang, L., Chen, R.: Study on vertical vibration and transmission characteristics of railway ballast using impact hammer test. Constr. Build. Mater. 316, 125898 (2022)CrossRef

15.

Jain, A.K., Rastogi, V., Agrawal, A.K.: Experimental investigation of vibration analysis of multi-crack rotor shaft. Procedia Eng. 144, 1451–1458 (2016)CrossRef

16.

Wang, P., Zhang, Y., Feng, L., Hou, W., Wang, J., Li, W., Feng, K.: Study on the pneumatic hammer phenomenon of aerostatic bearings based on the empirical mode method: Numerical and experimental analysis. Tribol. Int. 181, 108305 (2023)CrossRef

17.

Kim, M., Park, H.W., Lee, S.K.: Investigation of machining stability considering thermal and rotation effect: Effectiveness of impact excitation for a rotating spindle. Int. J. Precis. Eng. Manuf. 23(10), 1143–1162 (2022)CrossRef

18.

Melnyk, O., Onishchenko, O., Onyshchenko, S., Shumylo, O., Volyanskyy, S., Bondar, A., Cheredarchuk, N.: Application of fuzzy controllers in automatic ship motion control systems. Int. J. Electr. Comput. Eng. 13(4), 2088–8708 (2023)

19.

Kovtun, S., Ponomarenko, O., Nazarenko, O.: Quality of the information flow management at stochastic energy consumption conditions. Syst. Res. Energ. 3(74), 78–84 (2023)

20.

Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Technical provision of diagnostic systems. In: Diagnostic Systems for Energy Equipments, pp. 91–133 (2020)

21.

Wang, T., Celik, O., Catbas, F. N., Zhang, L.: Practical calibration techniques for the modal impact hammer. In: Sensors and Instrumentation, Volume 5: Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, pp. 23–29. Springer International Publishing (2015)

22.

Gharaibeh, M.: Identification of printed circuit boards mechanical properties using response surface methods. Microelectron. Int. 39(1), 38–47 (2022)CrossRef

23.

Will, D.T., Zhu, W.D.: Experimental modal analysis and operational deflection shape analysis of a cantilever plate in a wind tunnel with finite element model verification. In: Experimental Techniques, pp. 1–20 (2023)

24.

Onishchenko, O.A., Melnyk, O.M., Yarovenko, V.A., Aleksandrovska, N.I., Kurdiuk, S.V., Parmenova, D.G., Storchak, O.O.: Study of efficiency and advancement of marine engine oil purification and filtration technologies. J. Chem. Technol. 31(4), 762–774 (2023)

25.

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

26.

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

27.

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. In: Diagnostic Systems for Energy Equipments, pp. 1–22 (2020)

28.

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

29.

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

30.

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, September)

31.

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)

32.

Kedadouche, M., Thomas, M., Tahan, A.J.M.S.: A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis. Mech. Syst. Signal Process. 81, 88–107 (2016)CrossRef

33.

Roy, S.S., Dey, S., Chatterjee, S.: Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sens. J. 20(18), 10792–10800 (2020)CrossRef

34.

Javorskyj, I., Kravets, I., Matsko, I., Yuzefovych, R.: Periodically correlated random processes: application in early diagnostics of mechanical systems. Mech. Syst. Signal Process. 83, 406–438 (2017)CrossRef

35.

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

36.

Peng, D., Teng, W., Liu, Y.: Improved energy spectrum via spectral correntropy-based coherence-gram for bearing fault diagnosis. IEEE Trans. Instrum. Measur. (2023)

37.

Natarajan, S.: Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis. Int. J. Syst. Control Commun. 8(1), 57–71 (2017)

38.

Khan, S.A., Kim, J.M.: Automated bearing fault diagnosis using 2D analysis of vibration acceleration signals under variable speed conditions. Shock Vibr. (2016)

39.

Unal, M., Sahin, Y., Onat, M., Demetgul, M., Kucuk, H.: Fault diagnosis of rolling bearings using data mining techniques and boosting. J. Dyn. Syst. Meas. Contr. 139(2), 021003 (2017)CrossRef

40.

Dzyuba, V., Zaporozhets, A.: Mathematical approaches to forecasting and researching the technical state of cylindrical shells of energy objects’ elements based on vibration monitoring systems. In: Systems, Decision and Control in Energy III, pp. 107–119. Springer International Publishing, Cham (2021)

41.

Saidi, L., Ali, J.B., Fnaiech, F.: Application of higher order spectral features and support vector machines for bearing faults classification. ISA Trans. 54, 193–206 (2015)CrossRef

42.

Ming, A., Zhang, W., Fu, C., Yang, Y., Chu, F., Liu, Y.: L-kurtosis-based optimal wavelet filtering and its application to fault diagnosis of rolling element bearings. J. Vib. Control 30(7–8), 1594–1603 (2024)CrossRef

43.

Liang, K., Zhao, M., Lin, J., Ding, C., Jiao, J., Zhang, Z.: A novel indicator to improve fast kurtogram for the health monitoring of rolling bearing. IEEE Sens. J. 20(20), 12252–12261 (2020)CrossRef

44.

Gao, Z., Lin, J., Wang, X., Xu, X.: Bearing fault detection based on empirical wavelet transform and correlated kurtosis by acoustic emission. Materials 10(6), 571 (2017)CrossRef

45.

Hemmati, F., Orfali, W., Gadala, M.S.: Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl. Acoust. 104, 101–118 (2016)CrossRef

46.

Ren, B., Yang, M., Chai, N., Li, Y., Xu, D.: Fault diagnosis of motor bearing based on speed signal kurtosis spectrum analysis. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), pp. 1–6. IEEE (2019, August)

47.

Wang, Y., Peter, W.T., Tang, B., Qin, Y., Deng, L., Huang, T., Xu, G.: Order spectrogram visualization for rolling bearing fault detection under speed variation conditions. Mech. Syst. Signal Process. 122, 580–596 (2019)CrossRef

48.

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

49.

Obuchowski, J., Wyłomańska, A., Zimroz, R.: The local maxima method for enhancement of time–frequency map and its application to local damage detection in rotating machines. Mech. Syst. Signal Process. 46(2), 389–405 (2014)CrossRef

50.

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. In: Models and Measures in Measurements and Monitoring, pp. 1–31 (2021)

51.

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)

52.

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, December)

53.

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, pp. 617–622. Springer International Publishing, Cham (2021, August)

54.

Myslovych, M., Zvarych, V., Ostapchuk, L., Hyzhko, Y., Hutorova, M.: Some practical issues of creating teaching complexes providing informational support for multilevel diagnostic systems for electrotechnical equipment. Comput. Probl. Electr. Eng. 2(10), 21–27 (2020)

55.

Zvaritch, V.M., Zaitsev, I.O., Myslovych, M.V., Levytskyi, A.S., Zakusilo, S.A.: Some features of the systems for monitoring and diagnostic hydro units technical condition with considering smart grid technology. In: IOP Conference Series: Earth and Environmental Science, vol. 1254, No. 1, p. 012026. IOP Publishing (2023, October)

56.

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

57.

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

58.

Borucki, S., Frącz, P., Boczar, T., Zmarzły, D.: Diagnostics of power transformers cores using a modified vibroacoustic method. In: 2012 IEEE International Symposium on Electrical Insulation, pp. 179–183. IEEE (2012, June)

59.

Shengchang, J., Yongfen, L., Yanming, L.: Research on extraction technique of transformer core fundamental frequency vibration based on OLCM. IEEE Trans. Power Delivery 21(4), 1981–1988 (2006)CrossRef

60.

Rivera, H.L., García-Souto, J.A., Sanz, J.: Measurements of mechanical vibrations at magnetic cores of power transformers with fiber-optic interferometric intrinsic sensor. IEEE J. Sel. Top. Quantum Electron. 6(5), 788–797 (2000)CrossRef

61.

Bohachev, I., Kovtun, S., Kuts, Y., Sozonov, S., Khaidurov, V.: Enhanced phase method of signal detection for ultrasonic magnetostriction defectoscopy of power equipment. Syst. Res. Energy 2, 72–82 (2023)CrossRef

62.

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

63.

Wu, J.D., Chuang, C.Q.: Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals. NDT and E Int. 38(8), 605–614 (2005)CrossRef

64.

Ibarra-Zarate, D., Tamayo-Pazos, O., Vallejo-Guevara, A.: Bearing fault diagnosis in rotating machinery based on cepstrum pre-whitening of vibration and acoustic emission. Int. J. Adv. Manuf. Technol. 104, 4155–4168 (2019)CrossRef

65.

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

66.

Stief, A., Ottewill, J.R., Orkisz, M., Baranowski, J.: Two stage data fusion of acoustic, electric and vibration signals for diagnosing faults in induction motors. Elektronika ir Elektrotechnika 23(6), 19–24 (2017)CrossRef

67.

Hameed, Z., Hong, Y.S., Cho, Y.M., Ahn, S.H., Song, C.K.: Condition monitoring and fault detection of wind turbines and related algorithms: a review. Renew. Sustain. Energy Rev. 13(1), 1–39 (2009)CrossRef

68.

Elforjani, M.: Diagnosis and prognosis of real world wind turbine gears. Renewable Energy 147, 1676–1693 (2020)CrossRef

69.

Lin, Y., Zhuang, L., Xuan, L., Hong, W., Sun, T., Yang, P., Yan, B.: The research on condition monitoring and fault diagnosis method of wind power spindle bearing. J. Phys. Conf. Ser. 2254(1), 012048. IOP Publishing (2022, April)

70.

Lang, W., Hu, Y., Li, Q., Wireko-Brobby, A., Alkahtani, M.: ASSGCN model based vibration signal reconstruction in motor vibration testing. IEEE Trans. Ind. Electron. (2023)

71.

Ozevin, D., Greve, D.W., Oppenheim, I.J., Pessiki, S.P.: Resonant capacitive MEMS acoustic emission transducers. Smart Mater. Struct. 15(6), 1863 (2006)CrossRef

72.

Ono, K.: Calibration methods of acoustic emission sensors. Materials 9(7), 508 (2016)CrossRef

73.

Eaton, M., Pullin, R., Holford, K., Evans, S., Featherston, C., Rose, A.: Use of macro fibre composite transducers as acoustic emission sensors. Rem. Sens. 1(2), 68–79 (2009)CrossRef

74.

McLaskey, G.C., Glaser, S.D.: Acoustic emission sensor calibration for absolute source measurements. J. Nondestr. Eval. 31, 157–168 (2012)CrossRef

75.

Grosse, C.U., Ohtsu, M., Aggelis, D.G., Shiotani, T. (eds.): Acoustic emission testing: basics for research–applications in engineering. Springer Nature (2021)

76.

Sikorski, W.: Development of acoustic emission sensor optimized for partial discharge monitoring in power transformers. Sensors 19(8), 1865 (2019)CrossRef

77.

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)

78.

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

79.

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)

80.

Shyshko, Y., Cherniavskyi, A., Shyshko, D., Borychenko, O., Zaporozhets, A., Zaitsev, I.: Increasing the efficiency of loading devices for biomass boilers. Energies 17(3), 590 (2024)CrossRef

81.

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

82.

Hvozdeva, I., Myrhorod, V., Budashko, V., Shevchenko, V.: Problems of improving the diagnostic systems of marine diesel generator sets. In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 350–354. IEEE (2020, February)

83.

Zacharewicz, M., Kniaziewicz, T.: Method of evaluation of the technical condition of the Diesel-electric unit. Diagnostyka 20(2), 113–119 (2019)CrossRef

84.

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 and Monitoring, pp. 157–190 (2021)

85.

Casado, A.J., Nieto, F.J., Blázquez, F., Platero, C.A.: A monitoring system for diesel engine driven generators based on electric power output oscillation assessment. IEEE Trans. Ind. Appl. 53(3), 3182–3188 (2016)CrossRef

86.

Liu, Z., Zhang, K., Yao, X., Wei, H., Fang, S., Wang, T.: Monitoring system for diesel generator operation condition via sound signal. In: 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 298–302. IEEE (2022, January)

87.

Drewing, S., Witkowski, K.: Spectral analysis of torsional vibrations measured by optical sensors, as a method for diagnosing injector nozzle coking in marine diesel engines. Sensors 21(3), 775 (2021)CrossRef

88.

Mahtani, K., Guerrero, J.M., Beites, L.F., Platero, C.A.: Auto-adaptive stator ground fault protection for synchronous generators in diesel-electric locomotives. In: 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), pp. 116–122. IEEE (2023, August)

89.

Ahmad, Z., Prosvirin, A.E., Kim, J., Kim, J.M.: Multistage centrifugal pump fault diagnosis by selecting fault characteristic modes of vibration and using Pearson linear discriminant analysis. IEEE Access 8, 223030–223040 (2020)CrossRef

90.

Zaporozhets, A., Kataiev, D.: Method of compensating for instrumental uncertainty in measurements using a coordinate measuring arm. Syst. Res. Energy 1(76), 45–53 (2024)

91.

Zhiyong, L., Hongdong, Z., Ruili, Z., Kewen, X., Qiang, G., Yuhai, L.: Fault identification method of diesel engine in light of pearson correlation coefficient diagram and orthogonal vibration signals. Math. Probl. Eng. (2019)

92.

Nowicki, J., Hebda-Sobkowicz, J., Zimroz, R., Wyłomańska, A.: Dependency measures for the diagnosis of local faults in application to the heavy-tailed vibration signal. Appl. Acoust. 178, 107974 (2021)CrossRef

93.

Martchenko, B.G., Myslovitch, M.V., Zvaritch, V.N.: Vibration signal expert system for fault detection of power equipment rolling bearings. IFAC Proc. Vol. 32(2), 7861–7866 (1999)CrossRef

94.

Hertsyk, S., Gyzhko, Y., Zvaritch, V., Myslovych, M., Ostapchuk, L.: Features of the construction of autonomous diagnostic systems of electrical equipment, taking into account the modes of its operation. In: 2019 IEEE 20th International Conference on Computational Problems of Electrical Engineering (CPEE), pp. 1–3. IEEE (2019, September)

95.

Janjarasjitt, S., Ocak, H.A.S.A.N., Loparo, K.A.: Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. J. Sound Vib. 317(1–2), 112–126 (2008)CrossRef

96.

Klausen, A., Robbersmyr, K.G.: Cross-correlation of whitened vibration signals for low-speed bearing diagnostics. Mech. Syst. Signal Process. 118, 226–244 (2019)CrossRef

97.

Randall, R.B., Antoni, J., Chobsaard, S.: The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mech. Syst. Signal Process. 15(5), 945–962 (2001)CrossRef

98.

Laissaoui, A., Bouzouane, B., Miloudi, A., Hamzaoui, N.: Perceptive analysis of bearing defects (contribution to vibration monitoring). Appl. Acoust. 140, 248–255 (2018)CrossRef

99.

Ming, A.B., Qin, Z.Y., Zhang, W., Chu, F.L.: Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 41(1–2), 141–154 (2013)CrossRef

100.

Capolino, G.A., Antonino-Daviu, J.A., Riera-Guasp, M.: Modern diagnostics techniques for electrical machines, power electronics, and drives. IEEE Trans. Industr. Electron. 62(3), 1738–1745 (2015)CrossRef

101.

Bertenshaw, D.R., Smith, A.C., Ho, C.W., Chan, T., Sasic, M.: Detection of stator core faults in large electrical machines. IET Electr. Power Appl. 6(6), 295–301 (2012)CrossRef

102.

Frosini, L.: Novel diagnostic techniques for rotating electrical machines—a review. Energies 13(19), 5066 (2020)CrossRef

103.

Choi, S., Haque, M.S., Tarek, M.T.B., Mulpuri, V., Duan, Y., Das, S., Toliyat, H.A., et al.: Fault diagnosis techniques for permanent magnet AC machine and drives—a review of current state of the art. IEEE Trans. Transp. Electrif. 4(2), 444–463 (2018)

104.

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

105.

Mallat, J.: Understanding the null hypothesis (H0) in non-inferiority trials. Crit. Care 21(1), 101 (2017)CrossRef

106.

Solomon, H., Stephens, M.A.: Approximations to density functions using Pearson curves. J. Am. Stat. Assoc. 73(361), 153–160 (1978)CrossRef

107.

Fox, J.: Regression Diagnostics: An Introduction. Sage Publications (2019)

108.

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

109.

Kim, Y., Park, T., Kim, S., Kwak, N., Kweon, D.: Artificial intelligent fault diagnostic method for power transformers using a new classification system of faults. J. Electr. Eng. Technol. 14, 825–831 (2019)CrossRef

110.

Li, W., Monti, A., Ponci, F.: Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE Trans. Instrum. Meas. 63(11), 2651–2665 (2014)CrossRef

111.

Mellit, A., Tina, G.M., Kalogirou, S.A.: Fault detection and diagnosis methods for photovoltaic systems: a review. Renew. Sustain. Energy Rev. 91, 1–17 (2018)CrossRef

112.

Beyer, W.H.: Handbook of Tables for Probability and Statistics. CRC Press (2019)

113.

Klyuev, R.V., Morgoev, I.D., Morgoeva, A.D., Gavrina, O.A., Martyushev, N.V., Efremenkov, E.A., Mengxu, Q.: Methods of forecasting electric energy consumption: a literature review. Energies 15(23), 8919 (2022)CrossRef

114.

Hong, T., Fan, S.: Probabilistic electric load forecasting: a tutorial review. Int. J. Forecast. 32(3), 914–938 (2016)CrossRef

115.

Daliento, S., Chouder, A., Guerriero, P., Pavan, A.M., Mellit, A., Moeini, R., Tricoli, P.: Monitoring, diagnosis, and power forecasting for photovoltaic fields: a review. Int. J. Photoenergy (2017)

116.

Ahmed, A., Khalid, M.: A review on the selected applications of forecasting models in renewable power systems. Renew. Sustain. Energy Rev. 100, 9–21 (2019)CrossRef

117.

Ye, L., Xia, X., Chang, Z.: Dynamic prediction for accuracy maintaining reliability of superprecision rolling bearing in service. Shock Vibr. (2018)

Title: Experimental Studies of Statistical Diagnostics Information and Measuring Systems
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_8