Fault identification of electromechanical system combined with FDA and ML-KELM

Abstract:

To accurately identify the faults of electromechanical systems in a tank autoloader, a fault identification method combining functional data analysis (FDA) and multi-layer kernel extreme learning machine (ML-KELM) is proposed. Firstly, the feature information of time series data with smooth characteristics during the electromechanical system operation is mined from a functional perspective, and the change features of time series data are characterized as feature parameters from different spaces by functional principal component analysis and principal differential analysis. Secondly, the extracted features of the multi-sensor time series data are screened by Relief-F to obtain features strongly correlated with the classification. Finally, ML-KELM is used to perform deep feature learning on the strongly correlated features to achieve a more abstract feature representation, thereby realizing accurate fault identification. The fault identification experiment is carried out using an experimental setup consistent with the principle of the chain conveyor in a tank autoloader. Experimental results show that functional principal component analysis and principal differential analysis can extract effective fault features of time series data from different feature spaces, and the features extracted by the two methods are complementary. The ML-KELM with three hidden layers can realize more accurate fault identification based on the strongly correlated features in the multi-sensor time series data features. The proposed method proves to be feasible and effective, providing a reference for the research on fault identification of the electromechanical systems in the tank autoloader.