Abstract
Abstract To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.
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