Selective weighted multi-scale morphological filter for fault feature extraction of rolling bearings.

Morphological filtering shows effectiveness in vibration signal analysis because of its simplicity and efficiency. Considering that different structural elements have different effects on filtering results, a new multi-scale morphological filtering (MMF) method called selective weighted multi-scale morphological filter (SWMMF) is developed for integrating results of different scales based on adaptive weighting strategy. Firstly, four morphological operators (dilation-closing, closing-dilation, erosion-opening and opening-erosion) are integrated into a new combination difference morphological filter to strengthen effect of faulty component extraction. Secondly, this new morphological filter is further extended to multiple scales in order to overcome limitation of single scale filter. Finally, the filtered results of different scales are adaptively combined by using the whale optimization algorithm (WOA)-based selective weighting method. The effectiveness of multi-scale filter and selective weights is proved by comparing with single-scale and average weighting filter on simulation and real-world cases (bearing vibration signals with different defects). The testing results on vibration signals indicate that SWMMF is able to extract effectively defect frequency and the corresponding multiplication frequencies from bearing vibration signals with heavy noise. The testing results illustrate that SWMMF outperforms other representative MMFs (e.g., weighted multi-scale morphological gradient operator (WMMG), weighted multi-scale difference operator (WMDIF), weighted multi-scale average operator (WMAVG)) on impulsive feature extraction of bearing vibrations signals with various defects. Moreover, it is demonstrated that SWMMF has good applicability in bearing fault diagnosis due to setup of adaptive weights and selection of structure element.

Adaptive MOMEDA based on improved advance-retreat algorithm for fault features extraction of axial piston pump.

The fault information of axial piston pump bearings is inevitably submerged by violent natural periodic impulses. Therefore, an accurate extraction of fault impulses remains a challenging problem. A hybrid method of MOMEDA and TEO is proposed to extract periodic impulses in this study. Firstly, the deconvolution periods of multiple periodic components in the original vibration signals are analysed using Kurtosis. Then, an advance-retreat algorithm is used to optimize the filter length of MOMEDA. After multiple input parameters are determined adaptively, the MOMEDA is used to enhance the various periodic impulses respectively. Finally, TEO demodulation is employed to further obtain fault frequencies. Experimental vibration data is used to verify the advantages of this method for periodic impulses extraction. The results are then compared with traditional deconvolution and decomposition techniques to prove the superior performance of the proposed approach in terms of its better accuracy and reduced processing time.