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

Shows how to analyze, in great detail, the industrial operational status through spatio-temporal representation learning Covers how to establish robust monitoring models for industrial processes with irregular data Indicates how to adaptively update models in order to reduce frequent false alarms for dynamic processes Explains how to take the temporal correlation into consideration to develop an adaptive monitoring model for satisfying the dynamic behaviours of industrial processes