基于改进分类器动态选择算法的滚珠丝杠副状态识别OA北大核心CSTPCD

To improve the condition recognition rates of the ball screw,an improved dynamic classifier selection method is proposed.In this method,with neighborhood components analysis(NCA),the neighborhood of the test sample is defined accurately and adaptively without selecting the distance metric,and then the competence of each classifier in the multiple classifier system for accurately recognizing the testing task can be measured more exactly.Conse-quently,the classification accuracy is no longer restricted by the distance metric selection.The presented approach is applied to identify the health state of the ball screw.First,the AdaBoost algorithm is employed to create a back propagation(BP)neural networks pool.Then,to enhance the classification rates,the proposed dynamic classifier selection methodology is utilized to select the most suited classifier from the classifier pool for condition recognition according to the features extracted from the online signal.Experimental results show that the proposed method can identify the ball screw condition effectively with an accuracy of 97.22％,which is higher than that of the BP neural networks,AdaBoost,and the conventional dynamic classifier selection method.