Bearing Damage Detection of BLDC Motors Based on Current Envelope Analysis

[1] Altug, S., Chow, M.Y., Trussell, H.J. (1999). Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis. IEEETransactions on Industrial Electronics, 46 (6), 1069-1079.10.1109/41.807988Search in Google Scholar

[2] Manoharan, S.C., Veezhinathan, M., Ramakrishnan, S. (2008). Comparison of two ANN methods for classification of spirometer data. Measurement ScienceReview, 8 (3), 53-57.10.2478/v10048-008-0014-ySearch in Google Scholar

[3] Hasan, M.A., Reaz, M.B.I. (2012). Hardware prototyping of neural network based fetal electrocardiogram extraction. Measurement ScienceReview, 12 (2), 52-55.10.2478/v10048-012-0007-8Search in Google Scholar

[4] Naik, G.R., Kumar, D.K., Arjunan, S.P. (2010). Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques. Measurement Science Review, 10 (1), 1-6.10.2478/v10048-010-0001-ySearch in Google Scholar

[5] Hussain, M.S., Mamun, M. (2012). Effectiveness of the wavelet transform on the surface EMG to understand the muscle fatigue during. MeasurementScience Review, 12 (1), 28-33.10.2478/v10048-012-0005-xSearch in Google Scholar

[6] Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2011). Application of wavelet analysis in EMG feature extraction for pattern classification. Measurement Science Review, 11 (2), 45-52.10.2478/v10048-011-0009-ySearch in Google Scholar

[7] Kijewski-Correa, T., Kareem, A. (2006). Efficacy of Hilbert and wavelet transforms for time-frequency analysis. Journal of Engineering Mechanics, 132 (10), 1037-1049.10.1061/(ASCE)0733-9399(2006)132:10(1037)Search in Google Scholar

[8] Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2011). On Hilbert-Huang transform approach for structural health monitoring. Journal of IntelligentMaterial Systems and Structures, 17 (8), 721-728.Search in Google Scholar

[9] Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2006). A new envelope algorithm of Hilbert-Huang transform. Mechanical Systems and Signal Processing, 20 (8), 1941-1952.Search in Google Scholar

[10] da Silva, A.M., Povinelli, R., Demerdash, N.A.O. (2008). Induction machine broken bar and stator short circuit fault diagnostics based on three-phase stator. IEEE Transactions on Industrial Electronics, 55 (3), 1310-1318.10.1109/TIE.2007.909060Search in Google Scholar

[11] Rivas, E., Burgos, J.C., Garcia-Prada, J.C. (2009). Condition assessment of power OLTC by vibration analysis using wavelet transform. IEEE Transactionson Power Delivery, 24 (2), 687-694.10.1109/TPWRD.2009.2014268Search in Google Scholar

[12] Chow, M.-Y., Sharpe, R.N., Hung, J.C. (1983). On the application and design of artificial neural networks for motor fault. IEEE Transactions on IndustrialElectronics, 40 (2), 189-196.Search in Google Scholar

[13] Tamura, S., Tateishi, M. (1997). Capabilities of a fourlayered feedforward neural network: Four layers versus three. IEEE Transactions on Neural Networks, 8 (2), 189-196.Search in Google Scholar

[14] Huang, G.-B., Chen, L., Siew, C.-K. (2012). Universal approximation of extreme learning machine with adaptive growth of hidden nodes. IEEE Transactionson Neural Networks Learning System, 23 (2), 365-371.Search in Google Scholar

[15] Tripathy, M., Maheshwari, R.P., Verma, H.K. (2012). Power transformer differential protection based on optimal probabilistic neural network. IEEETransactions on Power Delivery, 25 (1), 102-112.Search in Google Scholar

[16] Perera, N., Rajapakse, A. (2012). Recognition of fault transients using a probabilistic neural-network classifier. In IEEE Power and Energy Society GeneralMeeting, 24-29 July 2011.Search in Google Scholar

[17] Wei, W., Feng, G., Li, Z., Xu, Y. (2005). Deterministic convergence of an online gradient method for BP neural networks. IEEE Transactions onNeural Networks, 16 (3), 533-540.Search in Google Scholar

[18] Cheng, J.S., Yu, D.J., Tang, J.S. (2008). Deterministic convergence of an online gradient method for BP neural networks. Mechanism and Machine Theory, 43 (6), 712-723.10.1016/j.mechmachtheory.2007.05.007Search in Google Scholar

[19] Yang, R.Q., Gao, X. (2006). Hilbert-Huang transformbased vibration signal analysis for machine health monitoring. IEEE Transactions on Instrumentationand Measurement, 55 (6), 2320-2329.Search in Google Scholar

[20] Shukla, S., Mishra, S., Singh, B. (2009). Empiricalmode decomposition with Hilbert transform for power-quality assessment. IEEE Transactions onPower Delivery, 21 (4), 2159-2165.10.1109/TPWRD.2009.2028792Search in Google Scholar

[21] Burgsteiner, H. (2006). Training networks of biological realistic spiking neurons for real-time robot control. IEEE Transactions on Power Delivery, 19 (7), 741-752.Search in Google Scholar

[22] Selaimia, Y., Moussaoui, A., Abbassi, H.A. (2006). Multi neural networks based approach for fault detection and diagnosis of a DC-Motor. NeuralNetwork World, 16, 369-379.Search in Google Scholar