Structural Health Monitoring, 2024; 23(1):410-420, SN 1475-9217; DOI:

Diversity Maximization-based Transfer Diagnosis Approach of Rotating Machinery

Daoming Shea, Jin Chena, Xiaoan Yanb, Xiaoli Zhaob, and Michael G. Pechtc
aJiangsu University, Zhenjiang, Jiangsu, China
bNanjing University of Science and Technology, Nanjing, Jiangsu, China
cCenter for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, United States

For more information about this article and related research, please contact Prof. Michael G. Pecht.


The existing transfer diagnosis methods based on entropy minimization are easy to lead to trivial solution. To solve this problem, a deep diversity maximization-based adversarial transfer diagnosis approach for rotating machinery is presented in this paper. Firstly, the deep convolution neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The diversity maximization strategy is taken to balance the entropy minimization, so as to avoid trivial local minimum. The categories predicted by nontrivial domain adaptation method are more diverse. Moreover, the entropy is conducted to evaluate the uncertainty of the predicted result of the classifier. Using this deterministic strategy based on entropy to adjust the domain discriminator. The experimental study demonstrates the effectiveness of the developed method.

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