Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images

Abstract Vibration failure is a common problem in most rotating machinery, and vibration fault diagnosis is an important means of ensuring stable equipment operation. The present work proposes a rotor vibration fault diagnosis approach that transforms multiple vibration signals into symmetrized dot pattern (SDP) images, and then identifies the SDP graphical feature characteristic of different vibration states using a convolutional neural network (CNN). SDP images reveal different vibration states in a simple and intuitive manner. In addition, a CNN can reliably and accurately identify vibration faults by extracting the feature information of SDP images adaptively through deep learning. The proposed approach is tested experimentally using a rotor vibration test bed, and the results obtained are compared to those obtained with an equivalent CNN-based image recognition approach using orbit plot images. The rotor fault diagnosis precision is improved from 92% to 96.5%.