Health Status Recognition of Gear Pump Based on Bayesian-LSTM

doi:10.3901/JME.2023.16.397

Abstract

Abstract: Accurate recognition of the health status for hydraulic pumps is part of preventive maintenance（PM）. A health status recognition model of external gear pumps based on Bayesian-Long short-term memorg（BDL） combines the Bayesian method and the deep learning algorithm to realize the health state recognition of the external gear pumps is proposed. Firstly, improved variational modal decomposition（IVMD） is used to decompose and reconstruct the vibration signal of the sample pump, whose features are extracted based on the time domain, frequency domain and time-frequency domain. After the selected features are normalized, the feature matrix representing the vibration signal is constructed. Finally, the feature matrix and labels are used to train the BDL model,and the external gear pump health status recognition model is obtained. In order to verify the superiority of the BDL in recognition for the health status of external gear pumps, a comparison with models of LSTM and RNN has carried. The results show that the prediction accuracy of BDL is more than 98.3%, which has obvious advantages than LSTM and RNN. The conclusion can provide a reference for the application of deep learning in status recognition of hydraulic components.

Key words: external gear pump, health status recognition, accelerated degradation test, bayesian-LSTM

CLC Number:

TH325

GUO Rui, WANG Tao, LI Yongtao, WANG Jianwei, CAI Wei, ZHAO Jingyi. Health Status Recognition of Gear Pump Based on Bayesian-LSTM[J]. Journal of Mechanical Engineering, 2023, 59(16): 397-405.

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