Adaptive local independent component analysis

作者：Yu Cheng Yi^*; Xiao Han; Liu An Zhong; Li You Rong

来源：Journal of Vibration and Shock, 2012, 31(14): 148-151.

• Fuzzy k-means
• Gear fault diagnosis
• High-dimensional
• Independent analysis
• Independent components
• k-Means algorithm
• Number of clusters
• One-dimensional time series
• Phase spaces
• Trajectory matrix

An adaptive local independent component analysis algorithm was proposed. In the algorithm, one dimensional time series was reconstructed into a high dimensional trajectory matrix, and then clustered into several clusters using K-means algorithm, in which the number of clusters and the location of each cluster were determined by agglomerative fuzzy K-means clustering. The independent component analysis was applied to each cluster and the trajectory matrix was projected into low-dimensional phase space to obtain low-dimensional data, which were sorted and put in the original order. The final noise reduction result was achieved after reconstructing the one dimensional time series from the sorted data. Comparing with the common method of local independent component analysis using clustering, the proposed algorithm is more adaptive and robust. The algorithm was validated to be effective by numerical simulations and its application in gear fault diagnosis. The results show that the algorithm is capable of dealing with this class of signals.

• 出版日期2012-7-28
• 单位武汉科技大学