Abstract
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.
Highlights
Because the generalized Lp/Lq norm applied in blind deconvolution performs well in extracting sparse features from noise signals [20], the generalized Lp/Lq norm sparse filtering method for pulse feature enhancement is applied to rolling bearing fault diagnosis successfully [21]
In order to solve the problem of low precision and low efficiency of OMGD method using the fast kurtogram algorithm to extract bearing fault features under strong background noise, the protrugram algorithm was proposed to determine the filter parameters for the filter design, and the designed filter coefficient was adopted as the initial value of minimum generalized Lp/Lq deconvolution (MGD) to achieve more efficient pulse feature enhancement
(2) By comparing CEEMDAN-OMGD and FIFDOMGD, it can be seen that the decomposition speed of fast iterative filter decomposition (FIFD) is significantly faster than CEEMDAN. is efficiency is very important for processing large-scale data
Summary
Because the generalized Lp/Lq norm applied in blind deconvolution performs well in extracting sparse features from noise signals [20], the generalized Lp/Lq norm sparse filtering method for pulse feature enhancement is applied to rolling bearing fault diagnosis successfully [21]. Considering the poor noise resistance of these demodulation methods, the paper [26] has proposed that the 1.5-dimension energy spectrum be applied to analyze the bearing fault, since it can reduce noise well and recover nonlinear features. Erefore, in order to maintain the performance of deconvolution when the signal is affected by strong background noise, this paper has proposed a combination of OMGD and FIFD. We used the 1.5-dimension Teager energy spectrum for demodulation analysis to complete the feature extraction of microfaults. With the proposed FIFD-OMGD-1.5-dimension Teager energy spectrum, we could extract the microfault features of rolling bearing efficiently and accurately even with serious interference of noise.
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