Research on fault diagnosis of rolling bearings based on conditional variance statistic and cross-correlation spectrum

In this paper, relationships between conditional variance statistic and the center frequency as well as the bandwidth of the filter are researched, and we find that conditional variance statistic is suitable for selecting the center frequency of the filter, but undesirable for selecting the bandwidth. Meanwhile, considering that the traditional iterative way to select the optimal resonance frequency band will be affected by the step size, we choose to fix the bandwidth at five times fault characteristic frequency, then Whale Optimization Algorithm (WOA) is utilized to select the optimal center frequency so as to give consideration to both computing efficiency and accuracy of the selection of center frequency. To further suppress the in-band noise, the filtered signal is analyzed by high order energy operator, and the optimal two energy operators are chosen based on Fault Characteristic Index (FCI) for cross-correlation spectrum analysis. Simulation and experimental results indicate that the proposed algorithm can extract the fault feature of rolling bearings under strong background noise effectively.

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