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

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.