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

[Display omitted] •A CNN model for symmetrized dot pattern (SDP) image recognition is proposed.•A feature extraction method based on SDP fused multi-sensor information is proposed.•Rotating machinery vibration state recognition using CNN with SDP Images is proposed.•The vibration state recognition model based on CNN with SDP has high accuracy. 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%.