Abstract: In order to improve the efficiency and precision of terminal guidance radar fault diagnosis, a fault diagnosis method based on attribute granulation clustering and echo state network (ESN) was proposed. Firstly, an attribute distinguishing ability index was defined by 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, and then fault attribute reduction was completed by selecting clustering center attributes. In order to improve the dynamic adaptability of ESN reservoir to samples, Bienenstock-Cooper-Munro (BCM) rule was introduced into the reservoir construction to train the connection weight matrix. Meanwhile, the L1/2-norm penalty term was added to the objective function in order to improve the sparsification efficiency, solving a numerical oscillation problem by using a smoothing L1/2 regularizer, the model was 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. The training time of the simulation experiment is only 8.98s, and the diagnostic accuracy can reach 95.2%.

Key words: guidance radar, attribute granulation clustering, echo state network (ESN), Bienenstock-Cooper-Munro (BCM) rule, fault diagnosis