Fault diagnosis of axial piston pump based on multi-source subdomain adaptation and sensor data fusion

Abstract The axial piston pump is the core component in hydraulic systems. Its condition monitoring and fault diagnosis are crucial to ensure the safe and reliable operation of hydraulic systems. However, most of the existing fault diagnosis methods for axial piston pumps use the same working condition data. In actual operation, axial piston pump often experience varying loads, and the collected data is typically correlated but has different distributions. Therefore, a transfer learning method of multi-source subdomain adaptation and sensor fusion (MSASF) is proposed for fault diagnosis of axial piston pump. The proposed MSASF has three modules, a shared feature extraction module, a domain-specific feature extraction module and an output decision module. Firstly, the adaptive weighted fusion of multi-sensor data features is realized by the shared feature extraction module and the common features of multi-source heterogeneous data are extracted. Secondly, in the domain-specific feature extraction module, multi-branch network is used to extract features of each pair of source and target domains and the local maximum mean difference is utilized to align the sub-domain distribution of each pair of source and target domains. Finally, in the output decision module, the distribution distance between each pair of source domain and target domain is calculated using maximum mean discrepancy to obtain its weighted score. Combined with the classification output of each source domain, the final diagnosis decision is made. A dataset was constructed for the axial piston pump design fault experiment, and four sets of transfer tasks were designed to compare with those of seven classic methods. The experimental results showed that the proposed MSASF method exhibits a superior domain adaptation effect and fault diagnosis performance.