Research Of Sequential Diagnosis Based On GA-NN For Rotating Machinery

The ability of rotating machinery's symptom parameters (SPS) in distinguishing two states is studied in this paper, the evaluation standard of SPS is put forward. Based on traditional SPS, a way of using genetic algorithm (GA) to create new and high SPS by self-organization traditional SPS is proposed, the feature set composed of all s SPS is selected by GA , and redundant symptoms are removed by this way. The sequential diagnosis model based on neural network (NN) is build up, the dimension of input is decreased. The practical example of condition diagnosis is shown to verify the efficiency of the method proposed in this paper. The diagnosis system combined evaluation of SPS sensitivity, feature extraction, feature selection and sequential diagnosis model. This paper has innovation and engineering value. The main results and conclusions is presented as follows:â‘ The sensitivity of SPS is proposed to evaluate the ability of distinguishing two states. And the formula sensitivity is derived theoretically. We verifies that parameters sensitivity is proportional to the distinction rate (DR). SPS with high sensitivity have best DR.â‘¡The sensitivity of traditional SPS is not high enough. We extract new and high sensitivity SPS with GA. SPS are self-organization and new SPS are created in this way. Arborescence is expressed to SPS formula and then intercrossed and variated by GA. Sensitivity of SPS is used as fitness.â‘¢In this paper, the feature set is composed of traditional SPS and new SPS created by GA. We use inner and outer distance as fitness function and select most effective SPS by GA. It can decrease the feature set's dimension and increase the precision of SPS distinguishing two states. Redundant SPS are removed.â‘£The sequential diagnosis model is build. Three SPS than have high sensitivity are selected and used as the inputs of sequential diagnosis neural network. Dimension of inputs is decreased.