关键词: 局部特征尺度分解, 局部切空间排列, 故障频率, 滚动轴承, 频谱分析

Abstract:

As the fault vibration signal characteristics presented non-stationary and the fault frequencies were hard to extracted, a new feature extraction method was proposed .This approach combined LCD and LTSA which was one of the typical manifold learning methods to extracting fault frequencies. Firstly, the vibration signals were decomposed into multiple intrinsic scale components in multidimensional feature vectors using LCD. Secondly, LTSA method was applied to compress the high-dimensional vectors into low-dimensional vectors, the low-dimensional vectors were used to reconstruct and the new fault signals were obtained. Finally, the new fault signal's spectrum were analysed and the fault characteristic frequencies were acquired. The rolling bearing fault experimental results show that this new technique may extract the inner and outer ring fault frequencies, it verifies the effectiveness of this new approach.

Key words: local characteristic-scale decomposition(LCD), local tangent space alignment(LTSA), fault frequency, rolling bearing, spectral analysis