The LFIgram: A Targeted Method of Optimal Demodulation Band Selection for Compound Faults Diagnosis of Rolling Bearing

As the main part of industrial rotating machinery, rolling bearings play an important role in improving the efficiency of mechanical equipment. Due to the influence of the complicated working environment, the single fault is easy to develop into the compound fault. The accurate identification of compound faults can effectively assess the severity of bearing damage, and can provide references for the continued use or replacement of bearings by technicians. The compound faults of rolling bearings are characterized by coupling, concealment, and complex resonance. For the diagnosis of compound faults, the “blind” methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, and some “target” methods cannot effectively extract fault features because they do not consider the influence of random slip of bearing. In order to solve this problem, the LFIgram method is proposed by constructing the log envelope autocorrelation slice bispectrum (LEAB) and LEAB feature index (LFI). The frequency band of the original signal is divided by the maximal overlap discrete wavelet packet transform (MODWPT), and the LFI index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault characteristic frequencies (FCFs), the resonant frequency band of the maximum LFI value is selected, the resonance band signal is analyzed by LEAB, and the fault type is identified according to the fault characteristic frequency in the LEAB. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed LFIgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent.