Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with
  Stochastic Configuration Networks
Wei Dai Jiang Liu Lanhao Wang
Fault diagnosis of rolling bearings is of great significance for
post-maintenance in rotating machinery, but it is a challenging work to
diagnose faults efficiently with a few samples. Additionally, faults commonly
occur with randomness and fuzziness due to the complexity of the external
environment and the structure of rolling bearings, hindering effective mining
of fault characteristics and eventually restricting accuracy of fault
diagnosis. To overcome these problems, stochastic configuration network (SCN)
based cloud ensemble learning, called SCN-CEL, is developed in this work.
Concretely, a cloud feature extraction method is first developed by using a
backward cloud generator of normal cloud model to mine the uncertainty of fault
information. Then, a cloud sampling method, which generates enough cloud
droplets using bidirectional cloud generator, is proposed to extend the cloud
feature samples. Finally, an ensemble model with SCNs is developed to
comprehensively characterize the uncertainty of fault information and advance
the generalization performance of fault diagnosis machine. Experimental results
demonstrate that the proposed method indeed performs favorably for
distinguishing fault categories of rolling bearings in the few shot scenarios.