Abstract: Blind source separation(BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. An under-determined BSS method is provided based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. The vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are determined according to the EMD result. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, blind source separation based on time-frequency analysis are used to the constructed signals. Simulation signal and gearbox signals are used to verify the effectiveness of this method. Compared with independent component analysis, BSS based on time-frequency analysis has good performance on signal separation. It is more suitable for weak feature extraction.

Key words: blind source separation;under-determined mixtures;time-frequency analysis;empirical mode decomposition;weak feature extraction