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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine;

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Monitoring Multimode Continuous Processes

This book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and;

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Monitoring Multimode Continuous Processes

This book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and;

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Condition Monitoring with Vibration Signals

improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes;

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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k;

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Fault-Diagnosis Applications: Model-Based Condition Monitoring

Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes;

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Chemical Process Performance Evaluation

Performance Evaluation introduces statistical methods and modeling techniques for process monitoring, performance evaluation, and fault diagnosis;

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Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems

Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes;

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Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

sensors Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas;

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Data-Driven Design of Fault Diagnosis and Fault-Tolerant Control Systems

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis;

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Data-Driven Fault Detection and Reasoning for Industrial Monitoring

of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as;

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Process Control System Fault Diagnosis A

of this book are to establish a new framework for control system fault diagnosis, to synthesize observations of different monitors with a prior;

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Power Plant Surveillance and Diagnostics

contributions through a thorough peer-reviewing process. Condition Monitoring and Early Fault Detection provide for better efficiency of energy systems;

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Vibration-based Condition Monitoring - Industrial, Automotive and Aerospace Applications, Second Edition

, since causal processing gives no benefit for condition monitoring A discussion of fault detection, diagnosis and prognosis in rotating and;

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Fault Detection

and diagnosis of discrete event systems using time petri nets; fault detection and diagnosis using statistical and soft computing methods.;

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Hands-On Unsupervised Learning with Python

. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best;

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Advanced Condition Monitoring and Fault Diagnosis of Electric Machines

. Advanced Condition Monitoring and Fault Diagnosis of Electric Machines is a collection of innovative research on various issues related to;

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Electric Machines

method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid;

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Electric Machines

method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid;

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Batch Fermentation: Modeling

real-time process supervision, and fault detection and diagnosis, Batch Fermentation offers valuable simulation and control;

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Machine Learning with scikit-learn Quick Start Guide

learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine;

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Applied Machine Learning for Smart Data Analysis

unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections;

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Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and;

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Knowledge-Driven Board-Level Functional Fault Diagnosis

and broad view of reasoning-based fault diagnosis system design. * Explains and applies optimized techniques from the machine-learning;

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Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;

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Intelligent Fault Diagnosis and Accommodation Control

continues to operate satisfactorily. In this book, we present a result of several years of work in the area of fault diagnosis and fault;

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Artificial Intelligence Tools

, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.;

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