Study on Health Indicator Construction and Health Status Evaluation of Hydraulic Pumps Based on LSTM-VAE

This paper addresses the difficulty of evaluating operating status in widely used gear pumps. A method for constructing hydraulic pump health indicators and evaluating health status is proposed based on LSTM-VAE. In this study, the vibration signal data source of gear pumps was assessed in the accelerated life test. Firstly, the normalized feature vectors of the whole-life operation data of gear pumps were extracted by wavelet packet decomposition and amplitude feature extraction. Combining an LSTM algorithm with a VAE algorithm, a method for constructing hydraulic pump health indicators based on LSTM-VAE is proposed. By learning the feature vectors of gear pumps in varying health conditions, a one-dimensional HI curve of the gear pumps was obtained. Then, LSTM was used to predict the HI curve of gear pumps. According to the volume efficiency of the gear pumps, the health status of gear pumps is divided into four states: health, sub-health, deterioration, and failure. The health status of the hydraulic pump is accurately evaluated by the health indicator. Finally, the proposed method is compared with the traditional method based on feature selection and PCA dimensionality reduction. The health indicator constructed by the method proposed in this paper is superior to the traditional method in terms of tendency, robustness, and monotonicity, which proves the effectiveness of the method proposed in this paper.

Automated summary

This paper addresses the difficulty of evaluating the operating status in widely used gear pumps and proposes a method for constructing hydraulic pump health indicators based on LSTM-VAE. The proposed method is compared with the traditional method and proves to be more effective in terms of tendency, robustness, and monotonicity.