1. bookVolume 15 (2015): Issue 4 (August 2015)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Open Access

Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

Published Online: 27 Aug 2015
Volume & Issue: Volume 15 (2015) - Issue 4 (August 2015)
Page range: 167 - 175
Received: 20 Jun 2014
Accepted: 24 Jul 2015
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

This paper proposes an approach based on acoustic signals for detecting faults appearing in synchronous motors. Acoustic signals of a machine were used for fault detection. These faults contained: broken coils and shorted stator coils. Acoustic signals were used to assess the usefulness of early fault diagnostic of synchronous motors. The acoustic signal recognition system was based on methods of data processing: normalization of the amplitude, Fast Fourier Transform (FFT), method of frequency selection (MoFS), backpropagation neural network, classifier based on words coding, and Nearest Neighbor classifier. A plan of study of acoustic signals of synchronous motors was proposed. Software of acoustic signal recognition of synchronous motors was implemented. Four states of a synchronous motor were used in analysis. A pattern creation process was carried out for 28 training samples of noise. An identification process was carried out for 60 test samples. This system can be used to diagnose synchronous motors and other electrical machines.

Keywords

[1] Bui, VP., Chadebec, O., Rouve, LL., Coulomb, JL. (2008). Noninvasive Fault Monitoring of Electrical Machines by Solving the Steady-State Magnetic Inverse Problem. IEEE Transactions on Magnetics, 44 (6), 1050-1053.10.1109/TMAG.2007.916593Search in Google Scholar

[2] Baranski, M., Decner, A., Polak, A. (2014). Selected Diagnostic Methods of Electrical Machines Operating in Industrial Conditions. IEEE Transactions on Dielectrics and Electrical Insulation. 21 (5), 2047-2054.10.1109/TDEI.2014.004602Search in Google Scholar

[3] Lu, C., Tao, XC., Zhang, WJ., Wang, ZL. (2014). Machine integrated health models for condition-based maintenance. Tehnicki Vjesnik-Technical Gazette, 21 (6), 1377-1383.Search in Google Scholar

[4] Krolczyk, GM., Krolczyk, JB., Legutko, S., Hunjet, A. (2014). Effect of the disc processing technology on the vibration level of the chipper during operations. Tehnicki Vjesnik-Technical Gazette, 21 (2), 447-450.Search in Google Scholar

[5] Krolczyk, G., Legutko, S. (2014). Investigations Into Surface Integrity in the Turning Process of Duplex Stainless Steel. Transactions of Famena, 38 (2), 77-82.Search in Google Scholar

[6] Kluska-Nawarecka, S., Wilk-Kolodziejczyk, D., Dajda, J., Macura, M., Regulski, K. (2014). Computerassisted integration of knowledge in the context of identification of the causes of defects in castings. Archives of Metallurgy and Materials, 59 (2), 743-746.10.2478/amm-2014-0124Search in Google Scholar

[7] Nawarecki, E., Kluska-Nawarecka, S., Regulski, K. (2012). Multi-aspect Character of the Man-Computer Relationship in a Diagnostic-Advisory System. Human-computer systems interaction: Backgrounds and applications 2. Pt 1, Book Series: Advances in Intelligent and Soft Computing, 98, 85-102.10.1007/978-3-642-23187-2_6Search in Google Scholar

[8] Niklewicz, M., Smalcerz, A. (2010). Application of three-coil cylindrical inductor in induction heating of gears. Przeglad Elektrotechniczny, 86 (5), 333-335.Search in Google Scholar

[9] Smalcerz, A. (2013). Aspects of application of industrial robots in metallurgical processes. Archives of Metallurgy and Materials, 58 (1), 203-209.10.2478/v10172-012-0174-5Search in Google Scholar

[10] Pribil, J., Pribilova, A., Frollo, I. (2014). Mapping and Spectral Analysis of Acoustic Vibration in the Scanning Area of the Weak Field Magnetic Resonance Imager. Journal Of Vibration And Acoustics- Transactions Of The Asme, 136 (5). DOI: 10.1115/1.4027791.10.1115/1.4027791Search in Google Scholar

[11] Sebok, M., Gutten, M., Kucera, M. (2011). Diagnostics of electric equipments by means of thermovision. Przeglad Elektrotechniczny, 87 (10), 313-317.Search in Google Scholar

[12] Pleban, D. (2014). Definition and Measure of the Sound Quality of the Machine. Archives of Acoustics, 39 (1), 17-23.Search in Google Scholar

[13] Zhao, Z., Wang, C., Zhang, YG., Sun, Y. (2014). Latest progress of fault detection and localization in complex Electrical Engineering. Journal of Electrical Engineering-Elektrotechnicky Casopis, 65 (1), 55-59.10.2478/jee-2014-0008Search in Google Scholar

[14] Glowacz, A., Glowacz, A., Glowacz, Z. (2015). Recognition of Monochrome Thermal Images of Synchronous Motor with the Application of Skeletonization and Classifier Based on Words. Archives of Metallurgy and Materials, 60 (1), 27-32.10.1515/amm-2015-0004Search in Google Scholar

[15] Zuber, N., Bajric, R., Sostakov, R. (2014). Gearbox faults identification using vibration signal analysis and artificial intelligence methods. Eksploatacja i Niezawodnosc-Maintenance and Reliability, 16 (1), 61-65.Search in Google Scholar

[16] Zhang, JH., Ma, WP., Lin, JW., Ma, L., Jia, XJ. (2015). Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence, Measurement, 59, 73-87.10.1016/j.measurement.2014.09.045Search in Google Scholar

[17] Wang, B., Liu, SL., Zhang, HL., Jiang, C. (2014). Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis. Journal of Vibroengineering, 16 (1), 57-69.Search in Google Scholar

[18] Xiao, H., Zhou, JZ., Xiao, J., Fu, WL., Xia, X., Zhang, WB. (2014). Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition. Journal of Vibroengineering, 16 (1), 487-498.Search in Google Scholar

[19] Tang, GJ., He, YL., Wan, ST., Xiang, L. (2014). Investigation on stator vibration characteristics under air-gap eccentricity and rotor short circuit composite faults. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 36 (3), 511-522.10.1007/s40430-013-0072-4Search in Google Scholar

[20] Amarnath, M., Krishna, IRP. (2014). Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis. Measurement, 58, 154-164.10.1016/j.measurement.2014.08.015Search in Google Scholar

[21] El-Thalji, I., Jantunen, E. (2015). A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 60-61, 252-272.10.1016/j.ymssp.2015.02.008Search in Google Scholar

[22] Kang, M., Kim, J., Kim, JM., Tan, ACC., Kim, EY., Choi, BK. (2015). Reliable Fault Diagnosis for Low- Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis. IEEE Transactions on Power Electronics, 30 (5), 2786-2797.10.1109/TPEL.2014.2358494Search in Google Scholar

[23] Wegiel, T., Sulowicz, M., Borkowski, D. (2007). A distributed system of signal acquisition for induction motors diagnostic, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, Cracow, POLAND, 88-92.10.1109/DEMPED.2007.4393105Search in Google Scholar

[24] Jedlinski, L., Caban, J., Krzywonos, L., Wierzbicki, S., Brumercik, F. (2015). Application of vibration signal in the diagnosis of IC engine valve clearance. Journal of Vibroengineering, 17 (1), 175-187.Search in Google Scholar

[25] Glowacz, A. (2014). 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 Niezawodnosc - Maintenance and Reliability, 16 (4), 554-558.Search in Google Scholar

[26] Glowacz, A. (2014). Diagnostics of synchronous motor based on analysis of acoustic signals with the use of line spectral frequencies and K-nearest neighbor classifier. Archives of Acoustics, 39 (2), 189-194.Search in Google Scholar

[27] Duspara, M., Sabo, K., Stoic, A. (2014). Acoustic emission as tool wear monitoring. Tehnicki Vjesnik- Technical Gazette, 21 (5), 1097-1101.Search in Google Scholar

[28] Moia, DFG., Thomazella, IH., Aguiar, PR., Bianchi, EC., Martins, CHR., Marchi, M. (2015). Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 37 (2), 627-640.10.1007/s40430-014-0191-6Search in Google Scholar

[29] Kulka, Z. (2011). Advances in Digitization of Microphones and Loudspeakers. Archives of Acoustics, 36 (2), 419-436.10.2478/v10168-011-0030-zSearch in Google Scholar

[30] The MARF Development Group. (2005). Modular Audio Recognition Framework v.0.3.0-devel-20050606 and its Applications, Application note.Search in Google Scholar

[31] Stepien, K. (2014). Research on a surface texture analysis by digital signal processing methods. Tehnicki Vjesnik-Technical Gazette, 21 (3), 485-493.Search in Google Scholar

[32] Pribil, J., Pribilova, A., Durackova, D. (2014). Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances Using the GMM Classifier. Journal of Electrical Engineering- Elektrotechnicky Casopis, 65 (1), 30-36.Search in Google Scholar

[33] Augustyniak, P., Smolen, M., Mikrut, Z., Kantoch, E. (2014). Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors. Sensors, 14 (5), 7831-7856.10.3390/s140507831406299724787640Search in Google Scholar

[34] Valis, D., Pietrucha-Urbanik, K. (2014). Utilization of diffusion processes and fuzzy logic for vulnerability assessment. Eksploatacja i Niezawodnosc- Maintenance and Reliability, 16 (1), 48-55.Search in Google Scholar

[35] Mazurkiewicz, D. (2014). Computer-aided maintenance and reliability management systems for conveyor belts. Eksploatacja i Niezawodnosc- Maintenance and Reliability, 16 (3), 377-382.Search in Google Scholar

[36] Kundegorski, M., Jackson, PJB., Ziolko, B. (2014). Two-Microphone Dereverberation for Automatic Speech Recognition of Polish. Archives of Acoustics, 39 (3), 411-420.Search in Google Scholar

[37] Jaworek-Korjakowska J., Tadeusiewicz R. (2014). Determination of border irregularity in dermoscopic color images of pigmented skin lesions. 2014 36TH Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6459-6462.10.1109/EMBC.2014.694510725571475Search in Google Scholar

[38] Rosner, A., Schuller, B., Kostek, B. (2014). Classification of Music Genres Based on Music Separation into Harmonic and Drum Components. Archives of Acoustics, 39 (4), 629-638.Search in Google Scholar

[39] Gorny, Z., Kluska-Nawarecka, S., Wilk- Kolodziejczyk, D., Regulski, K. (2015). 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), 309-312.10.1515/amm-2015-0050Search in Google Scholar

[40] Hachaj, T., Ogiela, MR. (2013). Application of neural networks in detection of abnormal brain perfusion regions. Neurocomputing, 122 (Special Issue), 33-42.10.1016/j.neucom.2013.04.030Search in Google Scholar

[41] Plawiak, P., Tadeusiewicz, R. (2014). Approximation of phenol concentration using novel hybrid computational intelligence methods. International Journal of Applied Mathematics and Computer Science, 24 (1), 165-181.10.2478/amcs-2014-0013Search in Google Scholar

[42] Jun, S., Kochan, O. (2014). Investigations of Thermocouple Drift Irregularity Impact on Error of their Inhomogeneity Correction. Measurement Science Review, 14 (1), 29-34.10.2478/msr-2014-0005Search in Google Scholar

[43] Roj, J. (2013). Neural Network Based Real-time Correction of Transducer Dynamic Errors. Measurement Science Review, 13 (6), 286-291.10.2478/msr-2013-0042Search in Google Scholar

[44] Krolczyk, JB. (2014). The Use of the Cluster Analysis Method to Describe the Mixing Process of the Multi- Element Granular Mixture. Transactions of Famena, 38 (4), 43-54.Search in Google Scholar

[45] Shariati, O., Zin, AAM., Khairuddin, A., Aghamohammadi, MR. (2014). Development and implementation of neural network observers to estimate synchronous generators' dynamic parameters using on-line operating data. Electrical Engineering, 96 (1), 45-54.10.1007/s00202-012-0274-2Search in Google Scholar

[46] Gokozan, H., Taskin, S., Seker, S., Ekiz, H. (2015). A neural network based approach to estimate of power system harmonics for an induction furnace under the different load conditions. Electrical Engineering, 97 (2), 111-117.10.1007/s00202-014-0320-3Search in Google Scholar

[47] Nafisi, H., Abedi, M., Gharehpetian, GB. (2014). Locating Pd in Transformers through Detailed Model and Neural Networks. Journal of Electrical Engineering-Elektrotechnicky Casopis, 65 (2), 75-82.10.2478/jee-2014-0011Search in Google Scholar

[48] Glowacz, A., Glowacz, A., Korohoda, P. (2012). Recognition of Color Thermograms of Synchronous Motor with the Application of Image Cross-Section and Linear Perceptron Classifier. Przeglad Elektrotechniczny, 88 (10A), 87-89.Search in Google Scholar

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