Wu, K., Kang, J.S. and Chi, K. (2016) Fault Diagnosis Method of Power Transformers Using Improved Multi-class Classification Algorithm and Relevance Vector Machine.High Voltage Engineering, 42, 3011-3017.
 Zhao, W.Q. and ZhuY.L. (2007) Summary of condition assessment for Power Transformer. Transformer, 44, 9-12.
 Cui Y., Ma, H. and Saha, T. (2016) Multi-source information fusion for power transformer condition assessment. Power and Energy Society General Meeting. Boston, 17-21, July 2016, 1-5.
 Gao, Z.X., Guo, C.G. and Yu, B. (2011) Study of A Fault Diagnosis Approach for Power Grid with Information Fusion Based on Multi-Data Resources. Power System Protection and Control, 39, 17-23.
 Dai, J. F. (2010) On-Line Fault Detection for Transformers Based on Information Fusion. Electrotechnics Electric, 51-55.
 Li, Y.W., Li, W. and Han, X.D. (2009) Application of Multi-Sensor Information Fusion Technology in the Power Transformer Fault Diagnosis. International Conference on Machine Learning and Cybernetics. Baoding, 12-15, July 2009, 29-33.
 Javed, K. (2014) ARobust And Reliable Data-Driven Prognostics Approach Based on Extreme Learning Machine and Fuzzy Clustering. Université de Franche-Comté.
 Tsui, K.L., Chen, N. and Zhou, Q. (2015) Prognostics and Health Management: A Review on Data Driven Approaches. Mathematical Problems in Engineering, 1-17.
 Shi, X., Zhu, Y.L. and Sa, C. (2016) Power Transformer Fault Classifying Model Based on Deep Belief Network. Power System Protection and Control, 44, 71-76.
 Hinton, G.E., Osindero, S. and The, Y.W. (2006)A Fast Learning Algorithm for Deep Belief Nets. Neural computation, 18, 1527-1554.
 Dempster, A. P. (1967) Upper and Lower Probabilities Induced by a Multivalued Mapping. The Annals of Mathematical Statistics,38, 325–339.