Abstract: Feature extraction is the basic work for data mining. The quality will largely affect the results of the excavation. The algorithm for LLE ( Locally Linear Embedding) does not consider the correlation between different characteristics of the same data, and can not well retain the main form trend of timing signals. We proposed CC-LLE ( Local Linear Embedding Algorithm Based on Characteristic Correlation) which is used to diagnosis of bearings. In response to the periodic characteristics of the bearing fault signal, the algorithm combines the data segmentation during the feature extraction stage. The standard deviations on each segment are selected as a characteristic to construct the characteristic sample set of the original data to effectively extract the identification characteristics. The experiments on the bearing data set proved the effectiveness of the algorithm in the feature extraction.

Key words: bearing fault diagnosis, local linear embedding, characteristic correlation, periodic time series