Wear State Identification Method for Axial Piston Pumps Based on Edge Computing

doi:10.3901/JME.2024.04.189

Abstract

Abstract: Wear state identification is of great significance to maintain the normal operation of axial piston pump. However, most existing fault diagnosis methods for axial piston pump are offline while those online are based on cloud computing, producing a long delay or a large amount of data, which cannot meet the real-time requirements of wear state identification. To cut down latency and data amount, a wear state identification method for axial piston pump based on edge computing is proposed. An edge node integrating the functions of data acquisition, data pre-processing, feature extraction and wear state classification is constructed to identify the wear state accurately and timely. Four kinds of wear states of axial piston pump are set as fault sources, and the corresponding wear fault dataset is established. To reduce the computational load of the edge node, the out-of-bag error of random forest is employed to select the sensitive features at the host computer. To classify the wear state, a feature-selected artificial neural network for wear state classification is trained at the host computer, and the data pre-processing and feature extraction algorithm together with model parameters are embedded into the edge node. The accuracy and real-time performance of the proposed method are demonstrated through comparisons with other methods and the online wear state identification experiment.

Key words: axial piston pump, edge computing, wear state identification, feature sensitivity, artificial neural network

CLC Number:

TH322

WANG Dandan, HUANG Weidi, ZHANG Junhui, ZHAO Shoujun, YU Bin, LIU Shihao, LÜ Fei, SU Qi, XU Bing. Wear State Identification Method for Axial Piston Pumps Based on Edge Computing[J]. Journal of Mechanical Engineering, 2024, 60(4): 189-199.

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