A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
By Te Han, Chao Liu, Wenguang Yang, Dongxiang Jiang. Knowledge-Based Systems, 2019
448 citations recorded by the source corpus.
Abstract In recent years, deep learning has become an emerging research orientation in the field of intelligent monitoring and fault diagnosis for industry equipment. Generally, the success of supervised deep models is largely attributed to a mass of typically labeled data, while it is often limited in real diagnosis tasks. In addition, the diagnostic model trained with data from limited conditions may generalize poorly for conditions not observed during training. To tackle these challenges, adversarial learning is introduced as a regularization into the convolutional neural network (CNN), and a novel deep adversarial convolutional neural network (DACNN) is accordingly proposed in this paper. By adding an additional discriminative classifier, an adversarial learning framework can be developed to train the convolutional blocks with the split data subsets, leading to a minimax two-player game. This process contributes to making the feature representation robust, boosting the generalization ability of the trained model as well as avoiding overfitting with a small size of labeled samples. The comparison studies with respect to conventional deep models on two fault datasets demonstrate the applicability and superiority of proposed method.
Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/4a2e34346b45feffbb1a73e810898fcf65006789
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In Mechanical Engineering
Citations in the corpus
- Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, unknown): “To tackle the challenge of limited labeled data, researchers applied a deep adversarial convolutional neural network to learn domain-invariant features for bearing fault diagnosis [146], [147], [148].”
- A survey on sentiment analysis methods, applications, and challenges (2022, background): “For example, Businesses can use the results of Sentiment Analysis to make product enhancements, examine consumer feedback, or develop a new marketing plan (Han et al. 2019).”
- Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing (2023, methodology): “Han et al. [29] proposed a cross-domain fault diagnosis method based on DANN, in which a domain discriminator was added to achieve domain-invariant learning.”