Research on fault detection of rolling bearings in press line by a new morphological filter based on diagonal slice spectrum lifting

Extraction of weak bearing failure signals remains a thorny issue in press lines. An adaptive enhanced morphological gradient operator (EMGO) fault feature diagnosis method based on third-order cumulant diagonal slice spectrum (AEMGO-TOCSS) is developed. Firstly, an EMGO is proposed to enhance the filtering ability of the operator according to the different features of the signal extracted by the basic morphological operator. Then, on account of the vital significance of structural element selection in filtering, a new adaptive feature energy per-mutation entropy (FEPE) selection strategy is put forward. Finally, the denoising performance of TOCSS is used to further improve the feature extraction ability of the EMGO operator for faulty information. Both simulation and experimental results verify that the EMGO is more effective and accurate than other morphological operators in identifying weak fault features of rolling bearings, and that it presents a broad application potential in practical engineering.

Automated summary

An adaptive enhanced morphological gradient operator (EMGO) fault feature diagnosis method based on third-order cumulant diagonal slice spectrum (AEMGO-TOCSS) is developed, which is more accurate and effective in extracting weak fault features of rolling bearings compared to other morphological operators.