Fault diagnosis model based on Granular Computing and Echo State Network

In order to improve the efficiency and accuracy of electronic equipment fault diagnosis, a fault diagnosis model based on Granular Computing and Echo State Network (ESN) is proposed. Firstly, the attribute reduction of test index is carried out based on granular computing model. An attribute distinguishing ability index is defined based on attribute value influence degree. As the basis of similarity measure, a number of attribute granules of similar distinguish are obtained through affinity propagation clustering algorithm, then fault attribute reduction was completed by selecting clustering center attributes. In the stage of fault identification by ESN, in order to improve the dynamic adaptability of ESN reservoir to samples, Bienenstock-Cooper-Munro(BCM) rule is introduced into the reservoir construction to train the connection weight matrix. Meanwhile, the L(1/)2-norm penalty term is added to the objective function in order to improve the sparsification efficiency, and a smoothing L-1/2-norm regularization term is used to overcome the iterative numerical oscillation problem, the model is solved by using the half threshold iteration method at last. The effectiveness and superiority of the proposed method are verified by a fault diagnosis example of terminal guidance radar signal processing module.