A grey fault diagnosis method for rolling bearings based on EMD

A rolling bearing vibration signal can be decomposed into a number of intrinsic mode functions (IMF) adaptively according to its own scale with the empirical mode decomposition (EMD) method. A rolling bearing failure will change distributions of IMF energy, and a bearing fault diagnosis can be realized by establishing the relationship between IMF energy distributions and bearing conditions based on the gray relational grade theory. Here, in order to improve the defects of the traditional gray analysis in pattern recognition, a gray similar relational grade model reflecting a curve's shape features was proposed based on the similarity of slope. Then, combined with the traditional approaching relativity model, a gray comprehensive relativity diagnosis model reflecting both a curve's position and its shape features was constructed. The simulation results showed that the new model can be used to recognize rolling bearing faults more effectively and accurately.