Sensors | Free Full-Text | Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty
In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN cluster...
Generative Adversarial Networks Gans Wasserstein Generative Adversarial Networks Wgans Slim Generative Adversarial Imputation Network Generative Adversarial Imputation Network Wasserstein Generative Adversarial Imputation Network Wasserstein Generative Adversarial Networks
Source: mdpi.com