Enhance the separation performance of ICA via clustering evaluation and its applications

Traditional independent component analysis may produce different results in the repeated calculations for their statistical characteristics, and thus they are unreliable and unstable. This paper introduces a novel ICA algorithm which enhances the separation performance and stability by clustering evaluation. Firstly, the improved ICA algorithm runs a single ICA algorithm for several times with the different initial parameters; secondly, the separated components produced are clustered according to their relevance; lastly, the best separated components are selected as the optimal results by clustering evaluation. The effectiveness of the improved ICA algorithm is validated in the simulation by typical mechanical signals. The proposed method is also applied to extract the effective information of observed signals on the bulkhead of a ship, and the results show that most of the important information is well extracted. This research provides a novel approach for vibration reduction and control of ships.