Aiming at the shortcomings of large feature set dimensionality, data redundancy and low fault recognition rate in existing fault diagnosis method based on simple combination of multi-classes features, a fault diagnosis method based on heterogeneous feature selection and fusion was proposed. The clustering characteristics of the feature data was analyzed according to the contours of the data of various class of features, and the redundant feature dimensions which are weakly clustered and not useful for fault classification were removed, only the feature dimensions with strong clustering characteristics were retained for the fault recognition. In the bearing fault diagnosis experiment, time-domain statistics and wavelet packet energy of fault signals were optimally selected and merged, and Back Propagation (BP) neural network was used for fault pattern recognition. The fault recognition rate reached 100%, which is significantly higher than that of the fault diagnosis method without feature selection and fusion. Experimental results show that the proposed method is easy to be implemented and can significantly improve the fault recognition rate.

Multiple feature fusion based tracking is one of the most active research topic in tracking field, but the tracking accuracy needs improving in complex environment and most of them use single fusion rule. In this paper, a new adaptive fusion strategy was proposed for multi-feature fusion. First, the local background information was introduced to strengthen the description of the target, and then the feature weight was calculated by a variety of criteria in the fusion process. In addition, the framework of mean shift was considered to realize target tracking. An extensive number of comparative experimental results show that the proposed algorithm is more stable and robust than the single fusion rule.