Rotating Machinery Faults Detection Method Based on Deep Echo State Network

This paper aims to develop an accurate and efficient end-to-end fault detection model trained by small-scale data for the rotating machinery. The echo state network (ESN) is promising thanks to the training process by linear regression, but it struggles in mining spatial information. Thus, a deep ESN based on fixed convolution kernels (FCK-DESN) is proposed. The Prewitt, the Sobel, and the Gaussian lowpass filters are designed as the fixed convolution kernels for spatial feature extraction without training. The one hidden layer autoencoder is built to compress the dimensionality and improve the applicability. Based on the pre-process modules, the ESN could realize pattern recognition under complex conditions. The fault detection approach is then constructed based on the time–frequency information provided by the smoothed pseudo-Wigner–Ville distribution. Case studies of a rotor-bearing system and a diesel engine show that the proposed FCK-DESN approach has better recognition rates than popular deep learning methods with high efficiency and lower data size requirements, which has more practical significance. • An FCK-DESN model based on the ESN, fixed convolution kernels, and the AE. • The SPWVD provides rich time–frequency information. • The FCK-DESN is robust to hyper-parameters, initial weights, and topology. • The model could detect rotating machinery faults end-to-end with a small dataset.