关键词: 端点效应, 故障诊断, 滚动轴承, 形态学解调, 奇异谱分解

Abstract:

Aiming at the difficulty of fault feature extraction of rolling bearing under strong background noise and interference sources, a morphological demodulation method based on improved singular spectrum decomposition (SSD) is proposed to detect bearing fault. Firstly, in order to avoid the empirical selection of embedding dimension length in singular spectrum analysis, a novel self-adaptive signal processing method named SSD is used to analyze the vibration signal. In this method, track matrix first is built and embedding dimension length is adaptively selected, and then several single components can be obtained by decomposing the non-stationary signal. However, boundary effect will appear in component reconstruction process of SSD. Aiming at this problem, a waveform matching extension method is used to improve the SSD, and several single components respectively located in high frequency to low frequency can be obtained, which are closer to the actual components curve. Besides, in order to extract the useful fault characteristic information of single components, a self-complementary top-hat transform is proposed to analyze the single components, which its optimal structure elements scale can be obtained by feature energy ratio. Finally, the fault types of bearing can be accurately identified by the spectrum analysis of morphological demodulation results. Simulation and practical signal analysis prove the validity of the proposed method.

Key words: end effect, fault diagnosis, morphological demodulation, rolling bearing, singular spectrum decomposition