Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions - Complex System Modelling and Control Through Intelligent Soft Computations - page 304

Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2000b). A review of process
fault detection and diagnosis part II: Qualitative models and search strategies. Computers and
Chemical Engineering, 27(3), 313 - 326.
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2000c). A review of process
fault detection and diagnosis part III: Process history based methods. Computers and Chemical
Engineering, 27(3), 327 346.

Verucchi, C. J., & Acosta, G. G. (2008). Fault detection and diagnosis techniques in induction
electrical machines. IEEE Transactions Latin America, 5(1), 41

-

49.
Wand, M. P., & Jones, M. C. (1994a). Kernel smoothing. United Kingdom: Chapman and Hall
CRC.
Wand, M. P., & Jones, M. C. (1994b). Multivariate plug-in bandwidth selection. Computational
Statistics, 9,97

-

116.
Yang, B.-S., & Kim, K. J. (2006). Application of Dempster-Shafer theory in fault diagnosis of
induction motors using vibration and current signals. Mechanical Systems and Signal
Processing, 20(2), 403

-

420.
Yu, J. (2011a). Bearing performance degradation assessment using locality preserving projections.
Expert Systems with Applications, 38(6), 7440 - 7450.
Yu, J. (2011b). Bearing performance degradation assessment using locality preserving projections
and Gaussian mixture models. Mechanical Systems and Signal Processing, 25(7), 2573 - 2588.
Zhaoxia, W., Fen, L., Shujuan, Y., & Bin, W. (2009). Motor fault diagnosis based on the vibration
signal testing and analysis. In Intelligent information technology application, 2009. IITA 2009.
Third International Symposium on (Vol. 2, pp. 433 - 436). Nanchang.

-