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Multivariate Statistical Monitoring of Industrial Processes

30 Chapters

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Industrial process monitoring landscape

why multivariate statistical methods are needed, typical process industries, and the role of data-driven monitoring in operation and safety.

Industrialmonitoring

Process data characteristics

sensor networks, sampling rates, autocorrelation, cross-correlation, nonstationarity, missing values, outliers, and data quality issues in industrial datasets.

characteristicsnetworks

Principal Component Analysis (PCA) for process monitoring

geometric interpretation, variance decomposition, loading and score matrices, and dimensionality reduction of correlated process variables.

PrincipalComponent

PCA monitoring statistics

Hotelling's T² and Squared Prediction Error (SPE), their distributions, control limits, and interpretation of fault detection results.

Building a PCA monitoring model from historical data

data preprocessing, scaling, model order selection, cross-validation, and practical implementation steps.

Fault detection with PCA

distinguishing normal variation from abnormal events, detection delay, false alarms, missed detections, and diagnostic plots such as score and loading plots.

Partial Least Squares (PLS) for process monitoring

handling quality-related variables, latent variable regression, and monitoring statistics for prediction and detection.

Dynamic PCA and dynamic PLS

augmenting data with time-lagged variables to capture process dynamics, and monitoring autocorrelated and dynamic industrial processes.

Canonical Variate Analysis (CVA) and subspace identification for process monitoring

state-space representation, canonical variates, and detection of dynamic anomalies.

CanonicalAnalysis

Independent Component Analysis (ICA) for non-Gaussian process monitoring

extracting independent components, monitoring statistics, and comparison with PCA-based methods.

IndependentComponent

Fault diagnosis and isolation

contribution plots, reconstruction-based contribution, fault direction identification, and isolating root causes of abnormal process behavior.

diagnosisisolation

Monitoring of batch and semi-batch processes

multiway PCA (MPCA), multiway PLS (MPLS), batch trajectory alignment, and online monitoring of batch evolution.

Monitoringsemi-batch

Monitoring of continuous processes with multiple operating modes

mode identification, mixture models, local PCA models, and mode-specific monitoring strategies.

Monitoringcontinuous

Adaptive and recursive monitoring

recursive PCA, moving window PCA, exponentially weighted PCA, and online updating for slowly varying process conditions.

Adaptiverecursive

Monitoring of nonstationary and time-varying processes

cointegration, slow feature analysis, and methods that separate normal process drifts from true faults.

Monitoringnonstationary

Nonlinear process monitoring

kernel PCA, kernel PLS, neural network-based autoencoders, and their application to nonlinear industrial systems.

Nonlinearmonitoring

Monitoring of large-scale and distributed processes

plant-wide monitoring, multiblock PCA/PLS, hierarchical monitoring, and distributed fault detection architectures.

Monitoringlarge-scale

Data-driven soft sensors and inferential monitoring

predicting hard-to-measure quality variables, soft sensor development, and integration with process monitoring systems.

Data-driveninferential

Fault detection and diagnosis in closed-loop control systems

interaction between controllers and monitoring, detection of sensor and actuator faults, and valve stiction diagnosis.

detectiondiagnosis

Monitoring of chemical reactors and unit operations

case studies on CSTR, distillation columns, and heat exchangers using multivariate statistical techniques.

Monitoringchemical

Monitoring of industrial processes with limited fault data

one-class classification, support vector data description, and semi-supervised monitoring approaches.

Monitoringindustrial

Big data and industrial IoT for process monitoring

data infrastructure, edge computing, streaming analytics, and scalable multivariate monitoring in smart manufacturing.

Deep learning for process monitoring

autoencoders, convolutional neural networks, recurrent neural networks, and their integration with statistical monitoring frameworks.

monitoringautoencoders

Evaluation and benchmarking of process monitoring methods

performance metrics, fault detection rates, false alarm rates, detection delay, and comparative case studies.

Evaluationbenchmarking

Industrial implementation and deployment

model maintenance, operator interface, alarm management, integration with DCS/SCADA, and practical challenges in real plants.

Industrialimplementation

Safety, risk, and alarm management in multivariate monitoring

alarm flood reduction, risk-based monitoring, and regulatory compliance in process industries.

managementmultivariate

Monitoring of energy and utility systems

power plants, boilers, compressors, and HVAC systems using multivariate statistical process control.

Monitoringcompressors

Monitoring of environmental and emission variables

multivariate monitoring for compliance, early warning of abnormal emissions, and integration with environmental management systems.

Monitoringenvironmental

Case studies and industrial applications

real-world examples from petrochemical, pharmaceutical, semiconductor, and water treatment processes.

industrialapplications

Emerging trends and future directions

explainable AI, digital twins, transfer learning, and next-generation multivariate statistical monitoring for industrial processes.

Emergingdirections