Deep Fuzzy Echo State Networks for Machinery Fault Diagnosis

Abstract

An echo state network (ESN) is a recurrent neural network with low computational complexity. However, a single ESN cannot extract effective features from complex inputs, especially for dealing with low-cost condition signals in machinery fault diagnosis. A novel deep learning model, referred to as the deep fuzzy ESN (DFESN), was proposed to improve the feature extraction capability with less computational burden. In the present method, the output data of the previous ESN reservoir were regarded as abstract feature vectors for the next ESN input. The features were reinforced in each hidden layer by using fuzzy clustering as a tuning step for classification enhancement. In this way, layerwise fuzzy tuning was developed to replace traditional overall feedback fine tuning in deep models. This improved learning efficiency and robustness while overcoming the vanishing gradient problem for deep learning. The superiority of the proposed approach was evaluated by both theoretical analysis and experimental tests. The results showed that the present DFESN features improved classification accuracy and reduced the computational burden. In addition to machinery fault diagnosis, the proposed DFESN also has potential for other deep learning applications.