Aiming at the problem of vibration signals collected by a single sensor possible to be a sum of several sources and hard to be separated, a method based on improved dictionary learning was proposed for single channel blind source separation of vibration signals.Firstly, the shift invariant dictionary learning algorithm was utilized to learn shift invariant base functions in signals.Then, shift invariant components (SIC) to reflect signals’ features in time domain and frequency one were obtained by reconstructing base functions.An adaptive fuzzy C-means clustering algorithm and the local maximum detection method were utilized to extract key points on envelope spectrum of each SIC obtained and these points were clustered.Finally, the clustered SICs were superimposed, respectively to acquire estimations of source signals.The tests of simulation data demonstrated that the proposed method has certain robustness in the presence of noise; it is used to conduct a certain type helicopter vibration signals separation, and verify its actual application value.
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