Yi Pan1, Lei Xie2,*, Hongye Su2
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 683-696, 2024, DOI:10.32604/iasc.2023.038543
Abstract In the process of fault detection and classification, the operation mode usually drifts over time, which brings great challenges to the algorithms. Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset, the accuracy of these traditional methods usually drops significantly in the case of covariate shift. In this paper, an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process. It effectively alters the drift between the training and testing dataset. Firstly, the mutual information method is… More >
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