Local Fault Assessment in a Helical Geared System via Sound and Vibration Parameters Using Multiclass SVM Classifiers
Aditya S., Amarnath M., Kankar P.K. (2016), Feature Extraction and Fault Severity Classification in Ball Bearings, Journal of Vibration and Control, 22, 1, 176–192.
Amarnath M., Sugumaran V., Hemantha Kumar (2013), Exploiting sound signals for fault diagnosis of bearings using decision tree, Measurement, 46, 1250–1256.
Amarnath M., Praveen Krishn I.R. (2014), Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis, Measurement, 58, 154–164.
Banerjee T.P., Das S. (2012), Multi-sensor data fusion using support vector machine for motor fault detection, Information Sciences, 217, 96–10, 96–108.
Collobert R., Bengio S. (2001), Suport vector machines for large scale regression problems, Journal of machine learning research, 1, 143–160.
Chen D., Wang W.J. (2002), Classification of wavelet patterns using multilayer neural networks, Mech. Syst. and Signal Process., 16, 4, 695–704.
Demuth, Beale (1998), User’s guide for neural network toolbox for use with MATLAB, The Mathworks Inc, Natick. 3.
Hu Q., He Z., Zhang Z ., Zi Y. (2007), Fault diagnosis of rotating machinery based on improved Wavelet packet transform and SVMs ensemble, Mech. Syst. Signal Process., 21, 2, 688–705.
Li B., Yuen M., Hung J.C. (2000), Neural network based motor rolling bearing fault diagnosis, IEEE Transaction on Industrial Electronics, 47, 5, 1060–1069.
Mc Cormick A.C., Nandi A.K. (1997), Classification of the rotating machine condition using artificial neural networks, Proc. Instn. Mech Engrs. 211, Part C, 439–450.
Murray A., Penman J. (1997), Extraction of useful higher order features for condition monitoring using artificial neural networks, IEEE Trans. on signal process., 45, 11, 2821–2828.
Namdari M., Hooshang J.-R. (2014), Incipient fault diagnosis using support vector machines based on monitoring continuous decision functions, Engineering Applications of Artificial Intelligence, 28, 22–35, 22–35.
Paya B.A., East, I.I., Badi M.N.M. (1999), Artificial neural network based fault diagnostics of rotating machinery using wavelet transform as a preprocessor, Mech. Syst. Signal Process., 11,5, 751–765.
Samanta B. (2004), Gear fault diagnosis using artificial neural networks and support vector mechanics with genetic algorithms, Mech. Syst. and Signal Process., 18, 3, 625–649.
Shin H.J., Eom D.H., Kim S.S. (2005), One class support vector machines – an application in fault detection and classification, Computer and Industrial Engineering, 48, 395–408.
Staszewski W.J., Worden K., Tomlinson G.R. (1997), Time-frequency analysis in gearbox fault detection using the Wigner-ville distribution and pattern recognition, Mech. Syst. Signal Process., 11, 5, 673–692.
Sugumaran V., Muralidharan V., Ramachandran K.I. (2007), Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing, Mech. Syst. Signal Process., 21, 2, 930–942.
Vyas N.S., Kumar D.S. (2001), Artificial neural network design for fault identification in a rotor-bearing system, Mechanism and machine theory, 36, 157–175.
Wuxing L., Tse P.W., Guicai Z., Tielin S. (2004), Classification of gear faults using cumulants and the radial basis function, Mech. Syst. Signal Process., 18, 381–389.
Wang W.J., Mc Fadden P.D. (1995), Application of orthogonal wavelets to early gear damage detection, Mech. Syst. Signal Process., 5, 5, 497–507.
Yang M., Stronach A.F., Mc Connel P. (2002), Third order spectral technique for the diagnosis of motor bearing conditions using artificial neural network, Mech. Syst. Signal Process., 16, 2-3, 391–411.
Yang B.S., Hwang W.W., Kim D.J., Tan A.C. (2005), Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines, Mech. Syst. Signal Process., 19, 371–390.
Yegnanarayana B. (1999) Artificial Neural Networks, 1st ed., Prentice Hall of India, New Delhi.
Yuan S.F., Chu F.L. (2006), Support vector machines – based fault diagnosis for turbo – pump rotor, Mech. Syst. Signal Process., 20, 939–952.
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