Enhancing Reliability and Performance of Deep GnG Monitoring Framework under Low-Quality Industrial Data

In the past years, the performance of latent variable models has been significantly improved by polishing their model structures, such as via kernel tricks, dynamic extensions, and even through deep learning techniques. It has become more difficult and cumbersome to explore further potentials from the aspect of model architecture. According to the recent perspective from data-centric artificial intelligence, improving the quality of the training dataset could be more effective in enhancing the performance of AI systems, compared to further polishing the model structure, which is already very complex. In this article, the reliability of the recently developed deep Gaussian and non-Gaussian (GnG) information fusion model is improved by designing an automatic data selection strategy and an iterative model updating scheme. By defining a new data quality monitoring index, those fault-free data samples are carefully selected from the raw dataset, which are then used for automatic updating of the monitoring framework. Based on the strict quality control of the training data samples, both monitoring performance and application reliability of the deep GnG model have been significantly improved. Besides, those expenses in terms of human labor and data labeling time can be largely saved at the same time.