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Full Description
Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.
The book not only discusses the complex models but also their real-world applications in industry.
Contents
1. Background
2. Low-rank characteristic and temporal correlation analytics for incipient industrial fault detection with missing data
3. A robust dissimilarity distribution analytics with Laplace distribution for incipient fault detection
4. Variational Bayesian Student's t-mixture model with closed-form missing value imputation for robust process monitoring of low-quality data
5. Stationary subspace analysis based hierarchical model for batch process monitoring
6. Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations
7. Incremental variational Bayesian Gaussian mixture model with decremental optimization for distribution accommodation and fine-scale adaptive process monitoring
8. MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes
9. Meticulous process monitoring with multiscale convolutional feature extraction
10. Summary and prospect
