A hybrid fault diagnosis method using morphological filter-translation invariant wavelet and improved ensemble empirical mode decomposition

作者：Meng, Lingjie; Xiang, Jiawei^*; Wang, Yanxue; Jiang, Yongying; Gao, Haifeng

来源：Mechanical Systems and Signal Processing, 2015, 50-51: 101-115.

DOI：10.1016/j.ymssp.2014.06.004

• Morphological filter
• Translation invariant wavelet
• Improved EEMD
• Rolling bearing
• Denoising
• Fault diagnosis

Defective rolling bearing response is often characterized by the presence Of periodic impulses, which are usually immersed in heavy noise. Therefore, a hybrid fault diagnosis approach is proposed. The morphological filter combining with translation invariant wavelet is taken as the pre-filter process unit to reduce the narrowband impulses and random noises in the original signal, then the purified signal will be decomposed by improved ensemble empirical mode decomposition (EEMD), in which a new selection method integrating autocorrelation analysis with the first two intrinsic mode functions (IMFs) having the maximum energies is put forward to eliminate the pseudo low-frequency components of IMFs. Applying the envelope analysis on those selected IMFs, the defect information is easily extracted. The proposed hybrid approach is evaluated by simulations and vibration signals of defective bearings with outer race fault, inner race fault, rolling element fault. Results show that the approach is feasible and effective for the fault detection of rolling bearing.

• 出版日期2015-1
• 单位桂林电子科技大学; 温州大学