Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes by Evan L Russell
Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques.
The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.
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Robust Autonomous Guidance
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Prototyping of Concurrent Control Systems Implemented in FPGA Devices
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Control of Variable-Geometry Vehicle Suspensions
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Robust Model Predictive Control for Autonomous Underwater Vehicles
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Model-Based Control of Mass–Stiffness–Damping Systems
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Pneumatic Servo Systems Analysis
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Control of Large Wind Energy Systems
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Predictive Functional Control
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Advanced Control and Supervision of Mineral Processing Plants
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Networked and Distributed Predictive Control
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Fault Detection and Fault-Tolerant Control Using Sliding Modes
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Reset Control Systems
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Industrial Process Identification and Control Design
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Cooperative Control of Multi-agent Systems
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Sliding-Mode Control of PEM Fuel Cells
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Control of Integral Processes with Dead Time
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Internet-based Control Systems
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Drives and Control for Industrial Automation
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PID Control in the Third Millennium
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Model Predictive Control of Wastewater Systems
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Structured Controllers for Uncertain Systems
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Fractional-order Systems and Controls
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Quad Rotorcraft Control
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Active Control of Flexible Structures
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Detection and Diagnosis of Stiction in Control Loops
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Motion Coordination for VTOL Unmanned Aerial Vehicles
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Vehicle-Manipulator Systems
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Optimal Control of Hybrid Vehicles
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Optimal Control and Optimization of Stochastic Supply Chain Systems
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Dynamics and Control of Switched Electronic Systems
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Hybrid Predictive Control for Dynamic Transport Problems
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Multivariate Statistical Process Control
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Model-Based Fault Diagnosis Techniques
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Adaptive Control of Solar Energy Collector Systems
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Practical Grey-box Process Identification
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Relay Tuning of PID Controllers
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Control of Solar Energy Systems
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Deadlock Resolution in Automated Manufacturing Systems
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Induction Motor Control Design
| SKU | Unavailable |
| ISBN 13 | 9781447111337 |
| ISBN 10 | 1447111338 |
| Title | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes |
| Author | Evan L Russell |
| Series | Advances In Industrial Control |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer London Ltd |
| Year published | 2012-11-01 |
| Number of pages | 192 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |