Intelligent Diagnosis and Prognosis of Industrial Networked Systems -

Description

In an era of intense competition where plant operating efficiencies must be maximized, downtime due to machinery failure has become more costly. To cut operating costs and increase revenues, industries have an urgent need to predict fault progression and remaining lifespan of industrial machines, processes, and systems. An engineer who mounts an acoustic sensor onto a spindle motor wants to know... Read more

In an era of intense competition where plant operating efficiencies must be maximized, downtime due to machinery failure has become more costly. To cut operating costs and increase revenues, industries have an urgent need to predict fault progression and remaining lifespan of industrial machines, processes, and systems. An engineer who mounts an acoustic sensor onto a spindle motor wants to know when the ball bearings will wear out without having to halt the ongoing milling processes. A scientist working on sensor networks wants to know which sensors are redundant and can be pruned off to save operational and computational overheads. These scenarios illustrate a need for new and unified perspectives in system analysis and design for engineering applications.

Intelligent Diagnosis and Prognosis of Industrial Networked Systems proposes linear mathematical tool sets that can be applied to realistic engineering systems. The book offers an overview of the fundamentals of vectors, matrices, and linear systems theory required for intelligent diagnosis and prognosis of industrial networked systems. Building on this theory, it then develops automated mathematical machineries and formal decision software tools for real-world applications.

The book includes portable tool sets for many industrial applications, including:

Forecasting machine tool wear in industrial cutting machines
Reduction of sensors and features for industrial fault detection and isolation (FDI)
Identification of critical resonant modes in mechatronic systems for system design of R&D
Probabilistic small-signal stability in large-scale interconnected power systems
Discrete event command and control for military applications

The book also proposes future directions for intelligent diagnosis and prognosis in energy-efficient manufacturing, life cycle assessment, and systems of systems architecture. Written in a concise and accessible style, it presents tools that are mathematically rigorous but not involved. Bridging academia, research, and industry, this reference supplies the know-how for engineers and managers making decisions about equipment maintenance, as well as researchers and students in the field.

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Vectors, Matrices, and Linear Systems
Fundamental Concepts
Vectors
Matrices
Linear Systems
Introduction to Linear Systems
State-Space Representation of LTI Systems
Linearization of Non-Linear Systems
Eigenvalue Decomposition and Sensitivity
Eigenvalue and Eigenvector
Eigenvalue Decomposition
Generalized Eigenvectors
Eigenvalue Sensitivity to Non-Deterministic System Parameters
Eigenvalue Sensitivity to Link Parameters
Singular Value Decomposition (SVD) and Applications
Singular Value Decomposition (SVD)
Norms, Rank, and Condition Number
Pseudo-Inverse
Least Squares Solution
Minimum-Norm Solution Using SVD
Boolean Matrices
Binary Relation
Graphs
Discrete-Event Systems
Conclusion

Modal Parametric Identification (MPI)
Introduction
Servo-Mechanical-Prototype Production Cycle
Modal Summation
Pole-Zero Product
Lumped Polynomial
Systems Design Approach
Modal Parametric Identification (MPI) Algorithm
Natural Frequencies fi and Damping Ratios ζi
Reformulation Using Forsythe’s Orthogonal Polynomials
Residues Ri
Error Analysis
Industrial Application: Hard Disk Drive Servo Systems
Results and Discussions
Conclusion

Dominant Feature Identification (DFI)
Introduction
Principal Component Analysis (PCA)
Approximation of Linear Transformation X
Approximation in Range Space by Principal Components
Dominant Feature Identification (DFI)
Data Compression
Selection of Dominant Features
Error Analysis
Simplified Computations
Time Series Forecasting Using Force Signals and Static Models
Determining the Dominant Features
Prediction of Tool Wear
Experimental Setup
Effects of Different Numbers of Retained Singular Values q and Dominant Features p
Comparison of Proposed Dominant Feature Identification (DFI) and Principal Feature Analysis (PFA)
Time Series Forecasting Using Acoustic Emission Signals and Dynamic Models
ARMAX Model Based on DFI
Experimental Setup
Comparison of Standard Non-Dynamic Prediction Models with Dynamic ARMAX Model
Comparison of Proposed ARMAX Model using ELS with DFI, MRM using RLS with DFI, and MRM using RLS with Principal Feature Analysis (PFA)
Effects of Different Numbers of Retained Singular Values and Features Selected
Comparison of Tool Wear Prediction Using AE Measurements and Force Measurements
DFI for Industrial Fault Detection and Isolation (FDI)
Augmented Dominant Feature Identification (ADFI)
Decentralized Dominant Feature Identification (DDFI)
Fault Classification with Neural Networks
Experimental Setup
Fault Detection Using 120 Features
Augmented Dominant Feature Identification (ADFI) and NN for Fault Detection
Decentralized Dominant Feature Identification (DDFI) and NN for Fault Detection
Conclusion

Probabilistic Small Signal Stability Assessment
Introduction
Power System Modeling: Differential Equations
Synchronous Machines
Exciter and Automatic Voltage Regulator (AVR)
Speed Governor and Steam Turbine
Interaction Between A Synchronous Machine and its Control Systems
Power System Modeling: Algebraic Equations
Stator Equations
Network Admittance Matrix YN
Reduced Admittance Matrix YR
State Matrix and Critical Modes
Eigenvalue Sensitivity Matrix
Sensitivity Analysis of the New England Power System
Statistical Functions
Single Variate Normal PDF of αi
Multivariate Normal PDF
Probability Calculations
Small Signal Stability Region
Impact of Induction Motor Load
Composite Load Model for Sensitivity Analysis
Motor Load Parameter Sensitivity Analysis
Parametric Changes and Critical Modes Mobility
Effect of the Number of IMs on Overall Sensitivity (with 30% IM load)
Effect On Overall Sensitivity with Different Percentages of IM Load in the Composite Load
Discussion
Conclusion

Discrete Event Command and Control
Introduction
Discrete Event C2 Structure For Distributed Teams
Task Sequencing Matrix (TSM)
Resource Assignment Matrix (RAM)
Programming Multiple Missions
Conjunctive Rule-Based Discrete Event Controller (DEC)
DEC State Equation
DEC Output Equations
DEC as a Feedback Controller
Functionality of the DEC
Properness and Fairness of the DEC Rule Base
Disjunctive Rule-Based Discrete Event Controller (DEC)
DEC Simulation and Implementation
Simulation of Networked Team Example
Implementation of Networked Team Example on Actual WSN
Simulation of Multiple Military Missions Using FCS
Conclusion

Future Challenges
Energy-Efficient Manufacturing
Life Cycle Assessment (LCA)
System of Systems (SoS)

References
Index

Author(s)

Biography

Chee Khiang Pang is an Assistant Professor in the Department of Electrical and Computer Engineering at National University of Singapore.

Frank L. Lewis is a Professional Engineer and Head of Advanced Controls and Sensors Group at the Automation and Robotics Research Institute, The University of Texas at Arlington.

Tong Heng Lee is Professor and cluster Head for the Department of Electrical and Computer Engineering at National University of Singapore.

Zhao Yang Dong is Associate Professor for the Department of Electrical Engineering at The Hong Kong Polytechnic University.

Intelligent Diagnosis and Prognosis of Industrial Networked Systems

Description

Table of Contents

Author(s)

Biography

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