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Deconvolution-related methods are the mainstream choice when it comes to enhancing the pulse impact of bearing fault and reducing noise interference. Kurtogram algorithm is used to optimize the minimum generalized Lp/Lq deconvolution to improve the nonconvexity of other optimization criteria. However, it has low computational efficiency and poor diagnostic accuracy under strong background noise. The paper proposes an optimized method using protrugram algorithm that combines fast iterative filter decomposition (FIFD) with minimum generalized Lp/Lq deconvolution (OMGD) for the 1.5-dimension Teager energy spectrum demodulation. Here is the specific process of the application: Fast iterative filtering (FIF) was used to reduce noise interference before using the maximum kurtosis to obtain the center frequency and frequency band and optimize the filter design, which was for the MGD initialization operation to prevent the result from falling into the local optimal solution and check the interference of impulse noise to a certain extent. The 1.5-dimension Teager energy spectrum was then used for demodulation analysis to extract small fault features of rolling bearings. The verification of simulation signals and actual data showed that this method was better in terms of extraction effect and efficiency than the use of fast kurtogram algorithm to optimize minimum generalized Lp/Lq deconvolution when it comes to extracting microfault features with high interference of background noise.

Application of FIFD-OMGD-1.5D Teager Energy to Extract Microfault Features of Rolling Bearing