Advances and opportunities in machine learning for process data analytics

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Sparse Principal Component Analysis

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Statistical process monitoring: basics and beyond

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A review on time series data mining

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Survey on data-driven industrial process monitoring and diagnosis

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Monitoring batch processes using multiway principal component analysis

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Review of Recent Research on Data-Based Process Monitoring

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Nonlinear PLS modeling using neural networks

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Process data analytics in the era of big data

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A new autocovariance least-squares method for estimating noise covariances

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Reinforcement Learning – Overview of recent progress and implications for process control

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Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures

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Neural networks for control

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A deep reinforcement learning approach for chemical production scheduling

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Big Data Analytics in Chemical Engineering

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Reducing data dimensionality through optimizing neural network inputs

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Semiconductor manufacturing process control and monitoring: A fab-wide framework

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Nonlinear Autoassociation Is Not Equivalent to PCA

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Soft-sensors for process estimation and inferential control

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Frontiers in Massive Data Analysis

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Real-time fault detection and diagnosis using sparse principal component analysis

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Regression on dynamic PLS structures for supervised learning of dynamic data

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Process control via artificial neural networks and reinforcement learning

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Concurrent quality and process monitoring with canonical correlation analysis

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Representation of process trends—IV. Induction of real-time patterns from operating data for diagnosis and supervisory control

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Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction

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Dynamic latent variable analytics for process operations and control

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Neural Networks for Intelligent Sensors and Control — Practical Issues and Some Solutions

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Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA

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Inference and Prediction

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Pattern Matching in Multivariate Time Series Databases Using a Moving-Window Approach

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Multi-Label Dimensionality Reduction

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Output Relevant Fault Reconstruction and Fault Subspace Extraction in Total Projection to Latent Structures Models

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Root cause diagnosis of quality-related faults in industrial multimode processes using robust Gaussian mixture model and transfer entropy

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Supervised linear dynamic system model for quality related fault detection in dynamic processes

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Reinforcement Learning – Overview of Recent Progress and Implications for Process Control

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Total PLS Based Contribution Plots for Fault Diagnosis

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Total PLS Based Contribution Plots for Fault Diagnosis

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Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks

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Special issue on big data: Data science for process control and operations

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A Quality-Relevant Monitoring Method for Closed-Loop Industrial Processes with Dual Consideration of Static and Dynamic Analysis

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Stock price prediction using deep learning and frequency decomposition

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Machine learning algorithms for social media analysis: A survey

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Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

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Perspectives on the integration between first-principles and data-driven modeling

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Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

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Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era

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Data Mining and Analytics in the Process Industry: The Role of Machine Learning

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Support industrial processes through machine learning

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A general paradigm for applying machine learning in automated manufacturing processes

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