Rolling Bearing Fault Diagnosis Method With Enhanced Top-Hat Transform Filtering and Cyclic Spectrum Coherence

As an important component of rotating machinery, the fault information of rolling element bearing is difficult to be recognized due to the background noise and harmonic frequency contained in the tested vibration signal. In order to accurately and completely extract the fault characteristic information from the vibration signal, a fault diagnosis research method (EAVGH-CSC-EES) based on the combination of enhanced top-hat morphological filtering (EAVGH) and cyclic spectrum coherence (CSC) is proposed. First of all, in view of the problem that the existing top-hat operators cannot fully extract the signal fault characteristics, this paper selects the optimal operator from the four enhanced morphology operators to construct the EAVGH. Since the reasonable selection of structural element (SE) scale has a great influence on the filtering result of morphological operators, then this paper applies feature energy factor (FEF) to select the optimal scale of SE. Subsequently, in order to further solve the influence of the non-linear modulation frequency components in the signal, while improving the filtering performance of EAVGH. This paper uses the cyclic spectrum coherence function (CSC) to further process the filtered signal. And then the enhanced envelope spectrum (EES) of the signal is obtained. Simulation and two sets of bearing fault experiments verify the rationality and effectiveness of the EAVGH-CSC method. The comparison results with other existing methods can further prove the superiority of the method proposed in this paper.