关键词: 多尺度熵, 二叉树, 多分类器, 故障诊断

Abstract: The fusion of multiple classifiers harnesses the advantages of various classification algorithms, and thus improves the robustness and accuracy of intelligent diagnosis models. When majority voting scheme is employed to construct a multi-classifier decision fusion system, the number of the required member classifiers is usually bound to be larger than that of the patterns to be recognized. Otherwise, it is difficult to achieve decision fusion in certain cases. Aiming at this issue, a multi-classifier fusion algorithm is presented using the form of binary tree, which transforms the multi-classification problem into a series of binary classification problems. In each binary classification, the number of member classifier is to be larger than 2, thus avoiding the requirement on large number of member classifiers. Experimental results demonstrate the proposed paradigm can effectively improve the recognition accuracy and stability of rolling bearing fault diagnosis in comparison with the diagnosis method based on a single classifier.

Key words: multiscale entropy, binary tree, multiple classifier, fault diagnosis