PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Recognition of rotor damages in a DC motor using acoustic signals

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Diagnosis of electrical direct current motors is essential for industrial plants. The emphasis is put on the development of diagnostic methods of solutions for capturing, processing and recognition of diagnostic signals. This paper presents a technique of early fault diagnosis of a DC motor. The proposed approach is based on acoustic signals. A real-world data of the DC motor were used in the analysis. The work provides an original feature extraction method called the shortened method of frequencies selection (SMoFS-15). The obtained results of the presented analysis show that the early fault diagnostic method can be used for monitoring electrical DC motors. The proposed method can also support other fault diagnosis methods based on thermal, current, and vibration signals.
Rocznik
Strony
187--194
Opis fizyczny
Bibliogr. 63 poz., rys., wykr., fot., tab.
Twórcy
autor
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, 30 Mickiewicza Av., 30-059 Krakow, Poland
autor
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, 30 Mickiewicza Av., 30-059 Krakow, Poland
Bibliografia
  • [1] K. Patan, “Artificial neural networks for the modelling and fault diagnosis of technical processes”, Lecture Notes in Control and Information Sciences 377, 1-206 (2008).
  • [2] H. Henao et al., “Trends in fault diagnosis for electrical machines a review of diagnostic techniques”, IEEE Industrial Electronics Magazine 8 (2), 31-42 (2014).
  • [3] W. Głowacz and Z. Głowacz, “Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier”, Archives of Electrical Engineering 64 (1), 29-35 (2015).
  • [4] M. Sulowicz, K. Weinreb, R. Mielnik, T. Zywczak, and M. Jaraczewski, “The method of current measurement in the rotor cage bars of prototype induction motor with the use of Rogowski coils”, International Conference on Information and Digital Technologies (IDT), 357-365 (2015).
  • [5] T. Ciszewski, L. Gelman, and L. Swedrowski, “Current-based higher-order spectral covariance as a bearing diagnostic feature for induction motors”, Insight 58 (8), 431-434 (2016).
  • [6] M. Michalak, B. Sikora, and J. Sobczyk, “Diagnostic model for longwall conveyor engines”, Man-Machine Interactions 4, ICMMI 391, 437-448 (2016).
  • [7] Z. Głowacz and A. Głowacz, “Simulation language for analysis of discrete-continuous electrical systems (SESL2)”, Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control, Innsbruck, Austria, 94-99 (2007).
  • [8] B. Będkowski and J. Madej, “The innovative design concept of thermal model for the calculation of the electromagnetic circuit of rotating electrical machines”, Eksploatacja i Niezawodność - Maintenance and Reliability 17 (4), 481-486 (2015).
  • [9] G. Singh, T.C.A. Kumar, and V.N.A. Naikan, “Induction motor inter turn fault detection using infrared thermographic analysis”, Infrared Physics & Technology 77, 277-282 (2016).
  • [10] M. Sebok, M. Gutten, and M. Kucera, “Diagnostics of electric equipments by means of thermovision”, Przegląd Elektrotechniczny 87 (10), 313-317 (2011).
  • [11] A. Głowacz and Z. Głowacz, “Diagnostics of stator faults of the single-phase induction motor using thermal images, MoASoS and selected classifiers”, Measurement 93, 86-93 (2016).
  • [12] L.X. Duan, M.C. Yao, J.J. Wang, T.B. Bai, L.B. Zhang, “Segmented infrared image analysis for rotating machinery fault diagnosis”, Infrared Physics & Technology 77, 267-276 (2016).
  • [13] H. Liu, Z.X. Wang, J. Zhong, and Z.W. Xie, “Early detection of spontaneous combustion disaster of sulphide ore stockpiles”, Tehnicki Vjesnik - Technical Gazette 22 (6), 1579-1587 (2015).
  • [14] R. Koprowski, “Some selected quantitative methods of thermal image analysis in Matlab”, Journal of Biophotonics 9 (5), 510-520 (2016).
  • [15] H. Glavas, L. Jozsa, and T. Baric, “Infrared thermography in energy audit of electrical installations”, Tehnicki Vjesnik - Technical Gazette 23 (5), 1533-1539 (2016).
  • [16] Z.X. Li, Y. Jiang, C. Hu, and Z. Peng, “Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review”, Measurement 90, 4-19 (2016).
  • [17] Y. Jiang, Z.X. Li, C. Zhang, C. Hu, and Z. Peng, “On the bi-dimensional variational decomposition applied to nonstationary vibration signals for rolling bearing crack detection in coal cutters”, Measurement Science and Technology 27 (6), (2016).
  • [18] D.H. Hwang, Y.W. Youn, J.H. Sun, K.H. Choi, J.H. Lee, and Y.H. Kim, “Support vector machine based bearing fault diagnosis for induction motors using vibration signals”, Journal of Electrical Engineering & Technology 10 (4), 1558-1565 (2015).
  • [19] C. Li, R.V. Sanchez, G. Zurita, M. Cerrada, and D. Cabrera, “Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning”, Sensors 16 (6), (2016).
  • [20] G.M. Krolczyk, J.B. Krolczyk, S. Legutko, and A. Hunjet, “Effect of the disc processing technology on the vibration level of the chipper during operations”, Tehnicki Vjesnik - Technical Gazette 21 (2), 447-450 (2014).
  • [21] G. Perun and Z. Stanik, “Evaluation of state of rolling bearings mounted in vehicles with use of vibration signals”, Archives of Metallurgy and Materials 60 (3), 1679-1683 (2015).
  • [22] Z. Stanik, G. Perun, and T. Matyja, “Effective methods for the diagnosis of vehicles rolling bearings wear and damages”, Archives of Metallurgy and Materials 60 (3), 1717-1724 (2015).
  • [23] W. Sawczuk, “Application of vibroacoustic diagnostics to evaluation of wear of friction pads rail brake disc”, Eksploatacja i Niezawodność - Maintenance and Reliability 18 (4), 565-571 (2016).
  • [24] L. Jedlinski, J. Caban, L. Krzywonos, S. Wierzbicki, and F. Brumercik, “Application of vibration signal in the diagnosis of IC engine valve clearance”, Journal of Vibroengineering 17 (1), 175-187 (2015).
  • [25] E. Carletti, G. Miccoli, F. Pedrielli, and G. Parise, “Vibroacoustic measurements and simulations applied to external gear pumps. An integrated simplified approach”, Archives of Acoustics 41 (2), 285-296 (2016).
  • [26] Z.X. Li, X.P. Yan, X.P. Wang, and Z.X. Peng, “Detection of gear cracks in a complex gearbox of wind turbines using supervised bounded component analysis of vibration signals collected from multi-channel sensors”, Journal of Sound and Vibration 371, 406-433 (2016).
  • [27] R. Lara, R. Jimenez-Romero, F. Perez-Hidalgo, and M.D. Redel- Macias, “Influence of constructive parameters and power signals on sound quality and airborne noise radiated by inverter- fed induction motors”, Measurement 73, 503-514 (2015).
  • [28] W. Caesarendra, B. Kosasih, A.K. Tieu, H.T. Zhu, C.A.S. Moodie, and Q. Zhu, “Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing”, Mechanical Systems and Signal Processing 72-73, 134-159 (2016).
  • [29] A. Deptuła, D. Kunderman, P. Osiński, U. Radziwanowska, and R. Włostowski, “Acoustic diagnostics applications in the study of technical condition of combustion engine”, Archives of Acoustics 41 (2), 345-350 (2016).
  • [30] A. Deptuła, P. Osiński, and U. Radziwanowska, “Decision support system for identifying technical condition of combustion engine”, Archives of Acoustics 41 (3), 449-460 (2016).
  • [31] D. Mika, J. Józwik, “Normative measurements of noise at CNC machines work stations”, Advances in Science and Technology Research Journal 10 (30), 138-143 (2016).
  • [32] J. Józwik, “Identification and monitoring of noise sources of CNC machine tools by acoustic holography methods”, Advances in Science and Technology Research Journal 10 (30), 127-137 (2016).
  • [33] M. Kunicki, A. Cichon, and S. Borucki, “Study on descriptors of acoustic emission signals generated by partial discharges under laboratory conditions and in on-site electrical power transformer”, Archives of Acoustics 41 (2), 265-276 (2016).
  • [34] D.P. Jena, S.N. Panigrahi, “Automatic gear and bearing fault localization using vibration and acoustic signals”, Applied Acoustics 98, 20-33 (2015).
  • [35] B. Van Hecke, J. Yoon, and D. He, “Low speed bearing fault diagnosis using acoustic emission sensors”, Applied Acoustics 105, 35-44 (2016).
  • [36] A. Głowacz, “Recognition of acoustic signals of synchronous motors with the use of MoFS and selected classifiers”, Measurement Science Review 15 (4), 167-175 (2015).
  • [37] P.A. Delgado-Arredondo, D. Morinigo-Sotelo, R.A. Osornio- Rios, J.G. Avina-Cervantes, H. Rostro-Gonzalez, and R.D. Romero-Troncoso, “Methodology for fault detection in induction motors via sound and vibration signals”, Mechanical Systems and Signal Processing 83, 568-589 (2017).
  • [38] A. Głowacz, “Diagnostics of Direct Current machine based on analysis of acoustic signals with the use of symlet wavelet transform and modified classifier based on words”, Eksploatacja i Niezawodność - Maintenance and Reliability 16 (4), 554-558 (2014).
  • [39] A. Głowacz, “Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers”, Tehnicki Vjesnik - Technical Gazette 23 (5), 1365-1372 (2016).
  • [40] A. Głowacz, “Recognition of acoustic signals of induction motors with the use of MSAF10 and Bayes classifier”, Archives of Metallurgy and Materials 61 (1), 153-157 (2016).
  • [41] V. Skrickij, M. Bogdevicius, and R. Junevicius, “Diagnostic features for the condition monitoring of hypoid gear utilizing the wavelet transform”, Applied Acoustics 106, 51-62 (2016).
  • [42] F. Hemmati, W. Orfali, and M.S. Gadala, “Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation”, Applied Acoustics 104, 101-118 (2016).
  • [43] M. He, D. He, and Y.Z. Qu, “A new signal processing and feature extraction approach for bearing fault diagnosis using AE sensors”, Journal of Failure Analysis and Prevention 16 (5), 821-827 (2016).
  • [44] W.J. Wang, L.L. Cui, and D.Y. Chen, “Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault”, Acta Mechanica Sinica 32 (2), 265-272 (2016).
  • [45] K. Stepien, “Research on a surface texture analysis by digital signal processing methods”, Tehnicki Vjesnik - Technical Gazette 21 (3), 485-493 (2014).
  • [46] D. Valis and K. Pietrucha-Urbanik, “Utilization of diffusion processes and fuzzy logic for vulnerability assessment”, Eksploatacja i Niezawodność - Maintenance and Reliability 16 (1), 48-55 (2014).
  • [47] D. Valis, L. Zak, and O. Pokora, “System condition estimation based on selected tribodiagnostic data”, Quality and Reliability Engineering International 32 (2), 635-645 (2016).
  • [48] R. Islam, S.A. Khan, and J.M. Kim, “Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors”, Journal of Sensors 2016 (2016), Article No. 7145715 (2016).
  • [49] P. Augustyniak, M. Smoleń, Z. Mikrut, and E. Kantoch, “Seamless tracing of human behavior using complementary wearable and house-embedded sensors”, Sensors 14 (5), 7831-7856 (2014).
  • [50] E. Dudek-Dyduch, R. Tadeusiewicz, and A. Horzyk, “Neural network adaptation process effectiveness dependent of constant training data availability”, Neurocomputing 72 (13-15), 3138-3149 (2009).
  • [51] J. Roj and A. Cichy, “Method of measurement of capacitance and dielectric loss factor using artificial neural networks”, Measurement Science Review 15 (3), 127-131 (2015).
  • [52] D. Panek, A. Skalski, J. Gajda, and R. Tadeusiewicz, “Acoustic analysis assessment in speech pathology detection”, International Journal of Applied Mathematics and Computer Science 25 (3), 631-643 (2015).
  • [53] D. Jamroz and T. Niedoba, “Application of multidimensional data visualization by means of self-organizing Kohonen maps to evaluate classification possibilities of various coal types”, Archives of Mining Sciences 60 (1), 39-50 (2015).
  • [54] T. Hachaj and M.R. Ogiela, “Application of neural network for human actions recognition”, Computational Intelligence and Intelligent Systems, ISICA, Communications in Computer and Information Science 575, 183-191 (2016).
  • [55] S. Jun and O. Kochan, “Investigations of thermocouple drift irregularity impact on error of their inhomogeneity correction”, Measurement Science Review 14 (1), 29-34 (2014).
  • [56] R.H.C. Palacios, I.N. da Silva, A. Goedtel, and W.F. Godoy, “A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors”, Electric Power Systems Research 127, 249-258 (2015).
  • [57] A. Yadav and A. Swetapadma, “A novel transmission line relaying scheme for fault detection and classification using wavelet transform and linear discriminant analysis”, Ain Shams Engineering Journal 6 (1), 199-209 (2015).
  • [58] C.P. Mbo’o and K. Hameyer, “Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection”, IEEE Transactions on Industry Applications 52 (5), 3861-3868 (2016).
  • [59] J. Jaworek-Korjakowska and P. Kleczek, “Automatic classification of specific melanocytic lesions using artificial intelligence”, BioMed Research International, Article No. 8934242 (2016).
  • [60] D. Wilk-Kolodziejczyk, K. Regulski, and G. Gumienny, “Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine”, International Journal of Advanced Manufacturing Technology 87 (1-4), 1077-1093 (2016).
  • [61] Z. Gorny, S. Kluska-Nawarecka, D. Wilk-Kolodziejczyk, and K. Regulski, “Methodology for the construction of a rule-based knowledge base enabling the selection of appropriate bronze heat treatment parameters using rough sets”, Archives of Metallurgy and Materials 60 (1), 309-312 (2015).
  • [62] E.P. Frigieri, P.H.S. Campos, A.P. Paiva, P.P. Balestrassi, J.R. Ferreira, and C.A. Ynoguti, “A mel-frequency cepstral coefficient- based approach for surface roughness diagnosis in hard turning using acoustic signals and Gaussian mixture models”, Applied Acoustics 113, 230-237 (2016).
  • [63] R.M. Bowen, F. Sahin, and A. Radomski, “Systemic health evaluation of RF generators using Gaussian mixture models”, Computers & Electrical Engineering 53, 13-28 (2016).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bc386e6c-2299-4148-b11d-0246b5f7cc15
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.