Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Fault diagnosis for wind turbine gearboxes by using deep enhanced fusion networkPublication . Pu, Ziqiang; Li, Chuan; Zhang, Shaohui; Bai, YunThe gearbox will directly affect the safety and reliability of the wind turbine, whose failure leads to low processing accuracy and certain economic losses. To address this issue, a deep enhanced fusion network (DEFN) is proposed for the fault diagnosis of the wind turbine gearbox with the experimental vibration data. In the proposed DEFN, three sparse autoencoders are first applied to extract deep features of three-axial vibration signals, respectively. Second, a feature enhancement mapping is developed to minimize the intraclass distance of the deep features in the three-axial vibration. Finally, the fused three-axis features are put into an echo state network for fault classification. The results of the experiment carried out in a wind turbine show that the proposed DEFN has a good fault diagnosis accuracy compared with other peer models.
- A one-class generative adversarial detection framework for multifunctional fault diagnosesPublication . Pu, Ziqiang; Cabrera, Diego; Bai, Yun; Li, ChuanIn this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging to use only normal data for fault diagnosis under this imbalanced condition. In addition, a single fault diagnosis model can only conduct one fault diagnosis task in most of cases. Accordingly, a one-class generative adversarial detection (OCGAD) framework based on semisupervised learning is proposed to learn one-class latent knowledge for dealing with multiple semisupervised fault diagnosis tasks, i.e., fault detection using only normal knowledge learning, novelty detection from unknown conditional data, and fault classification with unlabeled data. A bi-directional generative adversarial network (Bi-GAN) is first trained with only normal data. A one-class support vector machine is then established using features exacted by Bi-GAN from signals acquired from an attitude sensor for multifunctional fault detection. The presented OCGAD model is validated using an industrial robot with experiments of three fault detection tasks. The results demonstrate that the present model has good performance for dealing with multiple semisupervised diagnosis problems.