In an era of intensive 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.
Introduction Diagnosis and Prognosis Parametric-Based Non-Parametric-Based 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 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 i 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 i 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
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.