Fault diagnosis based on underdetermined blind source separation using potential energy function

This paper studied underdetermined blind source separation(UBSS),i.e.the number of mixtures is smaller than that of sources.A blind sparse source separation algorithm based on potential energy function was proposed to estimate both mixing matrix and the number of sources.In the algorithm,the problem of finding mixture signals’ linear clustering was transformed to that of finding the accumulative potential energy function’s local maximum by constructing potential energy function.The algorithm avoided the disadvantage of k-means clustering algorithm that the number of sources in UBSS should be given in advance.The advantage of the proposed algorithm was verified from simulation signals.Based on the assumption that the signal was sparse in frequency domain,the algorithm was applied to underdetermined blind separation of rolling bearing vibration fault signals.The results show the fault signals are separated effectively.