Fault Diagnosis of Piston Pump Based on KPEMD and INFO-SVM

Aiming at the problems such as difficult to effectively extract fault features from the nonlinear vibration signals of the piston pump of the transposition machine and the mode mixing phenomenon exists among the components, a fault diagnosis method combining kernel principal empirical mode decomposition（KPEMD） and support vector machine optimized by vector weighted average algorithm（INFO-SVM） was proposed. Firstly, the original signal was decomposed into multiple IMF components by KPEMD method, and the sensitive components with rich fault information were screened out according to the correlation coefficients. Secondly, the time domain and frequency domain features and energy entropy of the sensitive components were extracted to construct a mixed feature sample set. Finally, it is input into INFO-SVM multi-classifier for fault recognition. The results of comparative analysis using experimental data of piston pumps show that KPEMD can effectively attenuate the modal confusion phenomenon and fully extract the fault information, and the recognition accuracy of INFO-SVM is better than the optimization results of other common algorithms. This method can effectively identify different fault types of rutting machine plunger pumps, and the diagnosis accuracy reaches 98%.