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Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics

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3 Author(s)
Tallam, R.M. ; Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA ; Habetler, T.G. ; Harley, R.G.

The main limitations of neural network (NN) methods for fault diagnostics applications are training data and data memory requirements, and computational complexity. Generally, a NN is trained offline with all the data obtained prior to commissioning, which is not possible in a practical situation. In this paper, three novel and self-commissioning training algorithms are proposed for online training of a feedforward NN to effectively address the aforesaid shortcomings. Experimental results are provided for an induction machine stator winding turn-fault detection scheme, to illustrate the feasibility of the proposed online training algorithms for implementation in a commercial product.

Published in:

Power Electronics, IEEE Transactions on  (Volume:17 ,  Issue: 6 )

Date of Publication:

Nov 2002

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