Deep Learning-Based Cross-Machine Health Identification Method for Vacuum Pumps with Domain Adaptation

Ainapure, Abhijeet; Li, Xiang; Singh, Jaskaran; Yang, Qibo; Lee, Jay

Abstract Intelligent data-driven machinery health identification has been attracting increasing attention in the manufacturing industries, due to reduced maintenance cost and enhanced operation safety. Despite the successful development, the main limitation of most existing methods lies in the assumption that the training and testing data are collected from the same distribution, i.e. the same machine under identical condition. However, this assumption is difficult to be met in the real industries, since the diagnostic model is generally expected to be applied on new machines. In order to address this issue, a deep learning-based cross-machine health identification method is proposed for industrial vacuum pumps, which are of great importance in the manufacturing industry but have received far less research attention in the literature. Generalized diagnostic features can be learnt using the proposed domain adaptation technique with maximum mean discrepancy metric. The health identification model learnt from the training machines can be well applied on new machines. Experiments on a real-world vacuum pump dataset validate the proposed method, which is promising for industrial applications.