Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery,…

• M
  Mitacs
• NS
  Natural Sciences and Engineering Research Council of Canada
• BC
  British Columbia Knowledge Development Fund
• CR
  Core Research for Evolutional Science and Technology