Multiscale fluctuation dispersion entropy (MFDE) has been proposed to measure the dynamic features of complex signals recently. Compared with multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), MFDE has higher calculation efficiency and better performance to extract fault features. However, when conducting multiscale analysis, as the scale factor increases, MFDE will become unstable. To solve this problem, refined composite multiscale fluctuation dispersion entropy (RCMFDE) is proposed and used to improve the stability of MFDE. And a new fault diagnosis method for hydraulic pumps using particle swarm optimization variational mode decomposition (PSO-VMD) and RCMFDE is proposed in this paper. Firstly, PSO-VMD is adopted to process the original vibration signals of hydraulic pumps, and the appropriate components are selected and reconstructed to get the denoised vibration signals. Then, RCMFDE is adopted to extract fault information. Finally, particle swarm optimization support vector machine (PSO-SVM) is adopted to distinguish different work states of hydraulic pumps. The experiments prove that the proposed method has higher fault recognition accuracy in comparison with MSE, MFE, and MFDE.

The failure mechanism of hydraulic pump is complex, and its faulty features are frequently submerged in the nonlinear interference caused by various components. The fault diagnosis of hydraulic pump is a challenge in the field of machinery. The conventional fault diagnosis approaches have several drawbacks. First, the operator should be cognizant of the mechanism of hydraulic pump. Second, the procedure is onerous, and has many parameters to set. Third, the shallow classification is weak for this complex problem, which leads to low accuracy rate. This paper developed a new scheme by using improved convolutional neural network. It can be directly used without human intervention, although the operator knows little knowledge about hydraulic pump. Therefore, it is simple to be employed and easy for widely promotion. Validated by fault diagnosis cases of hydraulic pump, the proposed scheme is not only simple for application, but also is superior to other machine learning algorithms, especially when the pump speed varies.

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The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.