Cross-domain fault diagnosis of rotating machinery using discriminative feature attention network

Gye Bong Jang, Jin Young Kim, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In recent industrial applications, machine learning technology is proving useful in preventing equipment failures in advance through early failure diagnosis. In particular, we show that different domains can be linked through adversarial learning with data available in different working conditions to facilitate the training of the model, as it is impractical to acquire data for all conditions in real-world applications. Nevertheless, the initial failure is a difficult problem to diagnose because it does not show a significant difference from the normal data between different conditions. Moreover, if only the domain discriminator is judged when adapting the domain, it tends to easily cause misclassification, so the reliability of the detection result needs to be improved. In this study, we propose a new learning method that improves classification performance by sharing the classification characteristics of the classifier for each task with the target domain characteristic generator. The proposed mechanism uses spatial attention to extract the focused partial information of the feature generator and discriminator, and further enhances task-specific features using the attention mechanism between the two extracted information types. Addresses the challenge of implementing both domain adaptation and classification. Extensive experimentation demonstrates efficiency and improved classification performance on benchmark and real-world application datasets. In real machine cases, the classification accuracy is improved by almost 4%. In addition, the negative impact on false alarms was lowered by increasing the classification accuracy of minimum failures. Convincingly demonstrate model effectiveness by performing an empirical analysis of the method through ablation analysis and visualization.

Original languageEnglish
Article number9478860
Pages (from-to)99781-99793
Number of pages13
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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