Evolving Deep Echo State Networks for Intelligent Fault Diagnosis

Echo state network (ESN) is a fast recurrent neural network with remarkable generalization performance for intelligent diagnosis of machinery faults. When dealing with high-dimensional signals mixed with much noise, however, the performance of a deep ESN is still highly affected by the random selection of input weights and reservoir weights, resulting in the optimal design of the deep ESN architecture, which is an open issue. For this reason, a hybrid evolutionary algorithm featuring a competitive swarm optimizer combined with a local search is proposed in this article. An indirect encoding method is designed based on the network characteristics of ESN to make the evolutionary process computationally economical. A layerwise optimization strategy is subsequently introduced for evolving deep ESNs. The results of two experimental cases show that the proposed approach has promising performance in identifying different faults reliably and accurately by comparing with other intelligent fault diagnosis approaches.