- Tytuł:
- Multi-feature spatial distribution alignment enhanced domain adaptive method for tool condition monitoring
- Autorzy:
-
Hei, Zhendong
Sun, Bintao
Wang, Gaonghai
Lou, Yongjian
Yuqing, Zhou - Powiązania:
- https://bibliotekanauki.pl/articles/28328268.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Tematy:
-
condition monitoring
Transfer learning
correlation alignment
joint maximum mean difference
feature extractor - Opis:
- Transfer learning (TL) has been successfully implemented in tool condition monitoring (TCM) to address the lack of labeled data in real industrial scenarios. In current TL models, the domain offset in the joint distribution of input feature and output label still exists after the feature distribution of the two domains is aligned, resulting in performance degradation. A multiple feature spatial distribution alignment (MSDA) method is proposed, Including Correlation alignment for deep domain adaptation (DeepCORAL) and Joint maximum mean difference (JMMD). Deep CORAL is employed to learn nonlinear transformations, align source and target domains at the feature level through the second-order statistical correlations. JMMD is applied to improve domain alignmentby aligning the joint distribution of input features and output labels. ResNet18 combining with bidirectional short-term memory network and attention mechanism is developed to extract the invariant features. TCM experiments with four transfer tasks were conducted and demonstrated the effectiveness of the proposed method.
- Źródło:
-
Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 171750
1507-2711 - Pojawia się w:
- Eksploatacja i Niezawodność
- Dostawca treści:
- Biblioteka Nauki
Artykuł