An Imbalanced Data Augmentation and Assessment Method for Industrial Process Fault Classification With Application in Air Compressors

Abstract

Imbalanced data samples can adversely affect the performance of industrial process fault diagnosis models. Recently, it has become a valued challenge to expand data samples and reasonably assess their quality. To address this issue, this paper presents an imbalanced data augmentation and assessment method that integrates Wasserstein Time Generative Adversarial Network with Gradient Penalty (WTGAN-GP) and Maximum Information Coefficient with Improved Dynamic Time Warping Distance (MIC-IDTW) indicator. Firstly, the WTGAN-GP effectively tackles the scarcity of fault data by incorporating the Wasserstein distance with gradient penalty into TimeGAN, significantly enhancing the data generation capability and stability of the network. Additionally, the MIC-IDTW is established as a quantitative and interpretable indicator for assessing the quality of generated samples. Finally, this paper validates the performance of WTGAN-GP and MIC-IDTW in addressing the issue of imbalanced data in vibration fault diagnosis for an actual factory centrifugal air compressor. It is demonstrated that the proposed methods can effectively enhance various fault data in the presence of imbalanced fault samples and significantly improve the fault classification performance.