An optimal candidate fault frequency periodicity index optimization-gram for bearing fault diagnosis

The selection of optimal frequency band sensitive to fault is significant for bearing fault diagnosis. However, prior knowledge of fault characteristic frequency is usually essential in this operation. To address this issue, an optimal candidate fault frequency periodicity index optimization-gram is proposed. First, the spectral coherence theory is exploited to transform the vibration signal into a two-dimensional map consisting of cyclic and spectral frequencies. Second, a novel optimal candidate fault frequency periodicity index is constructed based on optimal candidate fault frequencies, which fully excavates the fault information hidden in a two-dimensional plane by utilizing modulation characteristics of bearing fault signal and transforms it into a specific numerical series. Then, the optimal candidate fault frequency periodicity index optimization-gram is further developed to identify the optimal frequency band, where the optimal candidate fault frequency periodicity index is utilized to quantify the fault information in the frequency bands separated by 1/3-binary tree filter bank. Finally, an improved envelope spectrum is obtained by integrating the spectral coherence over the optimal frequency band. The optimal candidate fault frequency periodicity index optimization-gram is demonstrated by simulated and experimental signals, and the results demonstrate that it is superior to other methods.