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In this paper a new comprehensive scheme for fault finding and logging for a compound progression is presented. This paper concentrates on the progression facts and skilful data, a trivial amount of labelled data and a large amount of unlabelled progression data are used for constructing the neighbourhood weighted graph. The optimal prediction label matrix of unlabelled data and the optimal regression function are obtained by solving the optimal problem. Then the monitoring data is projected to the low dimensional with the regression function, and the state label matrix of monitoring data can be acquired. This scheme can guarantee intrinsic structural of data by constructing an undirected weighted graph, and is suitable for the complex progression with nonlinear trait.

RETRACTED: CO-EXERCISE OF DT AND KNN CLASSIFIERS FOR FAULT DETECTION AND CLASSIFICATION OF SEMI SUPERVISED MACHINE LEARNING