A Deep Quality Monitoring Network for Quality-Related Incipient Faults

Although quality-related process monitoring has achieved the great progress, scarce works consider the detection of quality-related incipient faults. Partial least square (PLS) and its variants only focus on faults with larger magnitudes. In this article, a deep quality monitoring network (DQMNet) for quality-related incipient fault detection is developed. DQMNet includes the feature input layer, feature extraction layers, and the output layer. In the feature input layer, collected variables are divided according to quality variables, and then, features are extracted, respectively, through base detectors. For the feature extraction layers, singular values (SVs) of sliding-window patches and principal component analysis (PCA) are adopted to mine the hidden information layer by layer. For the output layer, statistics are constructed from quality-related/unrelated feature matrix through Bayesian inference. The superiority of DQMNet is demonstrated by a numerical simulation and the benchmark data of Tennessee Eastman process (TEP).

Automated summary

In this article, a deep quality monitoring network (DQMNet) is developed for the detection of quality-related incipient faults. DQMNet uses feature extraction and Bayesian inference to extract hidden information and construct statistics, demonstrating its superiority through numerical simulation and benchmark data.