Abstract: Aiming at series of problems such as weak fault signal, difficulty in extracting fault features and low fault diagnosis accuracy under the strong noise interference for loose slipper fault diagnosis of axial piston pump, a method of fault diagnosis of loose slipper fault diagnosis of axial piston pump based on minimum entropy deconvolution, ensemble empirical mode decomposition and extreme learning machine was proposed. Firstly, the vibration signal of the axial piston pump under normal and loose slipper conditions was collected. Then the MED method was applied to the vibration signal to eliminate noise interference and enhance impact characteristics. Afterwards, the denoised signal was decomposed into several intrinsic mode functions (IMFs) by EEMD, and the feature matrix was prepared by singularity value decomposition (SVD) of the obtained IMFs. Finally, the obtained feature matrix was input into the extreme learning machine, back propagation (BP) and support vector machine (SVM)classifiers. The recognition results were compared with those of the feature extraction method of EMD. The results indicate that the combination of MED and EEMD compensates for the limitation of EEMD extraction feature under strong background noise, and overcomes the defect that EMD is not sensitive to weak fault characteristics; the MED-EEMD feature extraction method and ELM classifier can be used to detect and diagnose the axial piston pump loose slipper fault in the case of a small number of samples.
Key words: minimum entropy deconvolution（MED）; ensemble empirical mode decomposition（EEMD）; extreme learning machine（ELM）; fault diagnosis