1st Edition

Intelligent Diagnosis and Prognosis of Industrial Networked Systems

    336 Pages 66 B/W Illustrations
    by CRC Press

    336 Pages 66 B/W Illustrations
    by CRC Press

    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.

    Diagnosis and Prognosis
    Applications in Industrial Networked Systems
    Modal Parametric Identification (MPI)
    Dominant Feature Identification (DFI)
    Probabilistic Small Signal Stability Assessment
    Discrete Event Command and Control

    Vectors, Matrices, and Linear Systems
    Fundamental Concepts
    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
    Least Squares Solution
    Minimum-Norm Solution Using SVD
    Boolean Matrices
    Binary Relation
    Discrete-Event Systems

    Modal Parametric Identification (MPI)
    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

    Dominant Feature Identification (DFI)
    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

    Probabilistic Small Signal Stability Assessment
    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

    Discrete Event Command and Control
    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

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



    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.