Research on a Multisource Domain Improved Fault Diagnosis Method of the Rotor System

Abstract

In this paper, rotor systems of the rotating machinery such as steam turbines, centrifugal compressors and flue gas turbines are selected as the research objects. At present, most of the rotor system fault diagnosis methods based on artificial intelligence algorithms are in the laboratory research stage, and there is still a gap from the actual industrial application. Therefore, the multi-source domain improved fault diagnosis (MSDIFD) method for satisfying engineering applications is proposed in this paper. Firstly, typical labeled data are selected to construct a multi-source domain training feature space. Then, commonality fault features are extracted and screened by the improved adaptive variational mode decomposition (IAVMD), and the feature reconstruction signal is automatically output. Next, the reinforced semi-supervised transfer component analysis (RSSTCA) method based on enhanced kernel function is employed to narrow the disparity between feature vectors of cross domain data. Finally, typical failure case data and real-time monitoring data are used as the training data and test data of the model, respectively, and an ensemble fault recognition classifier is constructed to achieve failure mode identification of the rotor system. Using 40 groups of typical fault engineering cases under different equipment and different operating conditions, the proposed rotor fault identification method has been verified and compared with five published fault identification methods. The results indicate that the proposed method possesses more excellent fault diagnosis accuracy and domain generalization performance, and the MSDIFD method has good application and promotion value for solving cross equipment, cross working condition, and cross domain diagnostic tasks.