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دسته بندی: فن آوری ویرایش: نویسندگان: Wolfgang Borutzky سری: ISBN (شابک) : 3030609669, 9783030609665 ناشر: Springer سال نشر: 2021 تعداد صفحات: 325 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 14 مگابایت
در صورت تبدیل فایل کتاب Bond Graph Modelling for Control, Fault Diagnosis and Failure Prognosis به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب مدل سازی نمودار پیوند برای کنترل، تشخیص خطا و پیش آگهی شکست نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
این کتاب در یک ارائه جامع نشان میدهد که چگونه متدولوژی Bond Graph میتواند از کنترل مبتنی بر مدل، تشخیص خطا مبتنی بر مدل، تطبیق خطا و پیشآگهی شکست با بررسی آخرین فناوری پشتیبانی کند. ، ارائه یک رویکرد یکپارچه ترکیبی برای تشخیص خطا و پیشبینی خرابی مبتنی بر مدل Bond Graph، و با ارائه مروری بر نرمافزاری که میتواند برای این کارها استفاده شود.
متن ساختار یافته نمونههای کوچک متعددی را نشان میدهد که چگونه ساختار محاسباتی که بر روی یک گراف پیوند علّی قرار گرفته میتواند برای بررسی ویژگیهای کنترلی مانند مشاهدهپذیری ساختاری و پایداری کنترل، انجام تخمین پارامتر و تشخیص و جداسازی خطا، ارائه مقادیر گسسته از روند تخریب ناشناخته در نقاط نمونه، و توسعه یک مدل معکوس مورد استفاده قرار گیرد. برای اسکان خطا این ارائه جامع همچنین پیشآگهی شکست را بر اساس تخمین حالت مستمر با استفاده از فیلترها یا پیشبینی سریهای زمانی پوشش میدهد.
این کتاب برای دانشجویان متخصص در زمینه همپوشانی مهندسی و علوم کامپیوتر و همچنین برای محققان و برای مهندسان صنعت که با مدلسازی، شبیهسازی، کنترل، تشخیص عیب و پیشبینی خرابی در زمینههای مختلف کار میکنند، نوشته شده است. زمینه های کاربردی و چه کسی ممکن است علاقه مند باشد که ببیند چگونه مدل سازی نمودار پیوند می تواند از کار آنها پشتیبانی کند.
This book shows in a comprehensive presentation how Bond Graph methodology can support model-based control, model-based fault diagnosis, fault accommodation, and failure prognosis by reviewing the state-of-the-art, presenting a hybrid integrated approach to Bond Graph model-based fault diagnosis and failure prognosis, and by providing a review of software that can be used for these tasks.
The structured text illustrates on numerous small examples how the computational structure superimposed on an acausal bond graph can be exploited to check for control properties such as structural observability and control lability, perform parameter estimation and fault detection and isolation, provide discrete values of an unknown degradation trend at sample points, and develop an inverse model for fault accommodation. The comprehensive presentation also covers failure prognosis based on continuous state estimation by means of filters or time series forecasting.
This book has been written for students specializing in the overlap of engineering and computer science as well as for researchers, and for engineers in industry working with modelling, simulation, control, fault diagnosis, and failure prognosis in various application fields and who might be interested to see how bond graph modelling can support their work.
Preface Contents Abbreviations 1 Introduction 1.1 Motivation 1.2 Organisation of the Book References 2 Structural Properties of Bond Graphs for Model-Based Control 2.1 Structural Observability and Structural Controllability 2.1.1 Bond Graph-Based Analysis of Structural State Observability 2.1.2 Bond Graph-Based Analysis of Structural State Controllability Example: Masses-Spring Oscillator Check for Structural Controllability on a Bond Graph Check for Structural Observability on a Bond Graph Example: RC Network 2.2 Transfer Functions Example: LC Network 2.2.1 Mason's Loop Rule 2.2.2 Application of Mason's Loop Rule Directly on a Causal Bond Graph Example: DC Motor Drive 2.3 Bond Graphs and Block Diagrams 2.4 Bicausal Bond Graphs 2.5 Parameter Estimation Based on Bicausal Bond Graphs Example 1: Parameter Estimation Applied to a Two-Tank System Controllability and Observability Estimating the Resistance of Valve 1 Example 2: Two-Tank System with Two Pressure Sensors 2.6 Inverse System Models 2.7 System Inversion Based on Bicausal Bond Graphs Example: Bicausal Bond Graph-Based System Inversion Applied to a RLC Circuit 2.8 Bond Graph-Based Stability Analysis Illustrative Example 1 Illustrative Example 2 2.9 Summary References 3 Fault Diagnosis 3.1 Types of Faults 3.2 Signal Preprocessing 3.2.1 Savitzky–Golay Filter 3.2.2 State Variable Filters 3.3 Data-Driven Methods 3.4 Filters for Estimating the State of Health of a System 3.4.1 Discrete-Time Linear Kalman Filter 3.4.2 Particle Filters 3.5 Bond Graph Model-Based Fault Detection and Isolation 3.5.1 Observer-Based Fault Detection 3.5.2 Fault Detection and Isolation Based on Analytical Redundancy Relations Derived from a Bond Graph 3.5.3 Avoiding Differentiation of Measurements 3.5.4 Parametric Fault Isolation and Fault Estimation 3.6 Robustness with Regard to Parameter Uncertainties 3.6.1 Uncertain BGs 3.6.2 BGs in Linear Fractional Transformation Form 3.6.3 Incremental BGs and Adaptive Fault Thresholds 3.7 Measurement Uncertainties, Sensor Faults, and Actuator Faults 3.7.1 Accounting for Measurement Uncertainties and Sensor Faults in a BG 3.7.2 Representing Actuator Faults in a BG 3.8 Sensor Placement on Diagnostic Bond Graphs and Fault Isolation 3.8.1 Graphical Approach to Sensor Placement and Fault Isolation 3.8.2 Faulty Sensors 3.8.3 Hybrid Models 3.9 Summary References 4 Failure Prognostic 4.1 Introduction 4.2 Data-Driven Failure Prognostic 4.2.1 Stochastic Data-Driven Methods Bayesian Networks 4.2.2 Statistical Data-Driven Methods Linear Regression Recursive Least Square Method Forgetting Factor Recursive Least Square Method Sliding Window Recursive Least Squares ARMA Parameter Estimation Combining Identification of a System with Deteriorating Behaviour and Failure Prognostic Based on Kalman Filtering 4.2.3 Neural Networks 4.3 Model-Based Failure Prognostic Physics-Based Failure Prognostic Hybrid Failure Prognostic 4.4 Determination of a Degradation Model from ARRs 4.5 A Hybrid Bond Graph Model-Based Data-Driven Approach 4.5.1 Bicausal Bond Graph-Based Online Estimation of Unknown Degradation Data Example: Boost Converter Fault Detection and Isolation Estimating the Unknown Degradation of the Load Resistance Estimating the Unknown Parameter Degradation of a Storage Element 4.5.2 ARR-Based Estimation of Degradation Dataon Two DBGs Example: Boost Converter Derivation of ARRs from the First Stage DBG Determination of Degradation Functions from ARRs of the Second Stage DBG 4.5.3 Learning a Mathematical Degradation Model 4.5.4 Projection and RUL Estimation Accuracy of Regression and Prediction Failure Prognostic for Hybrid Systems 4.6 Uncertainties in Hybrid Failure Prognostic Modelling Uncertainties Measurement Uncertainties Statistical and Environment Uncertainties Degradation Model Uncertainties Prediction Uncertainties Prognostic Metrics Risk Assessment Failure Threshold Onset of the Degradation and Start of the Prediction Some Advantages of the Presented Hybrid Method 4.7 Summary References 5 Fault Tolerant Control 5.1 Introduction Bond Graph Modelling and FTC 5.2 Fault Accommodation Using an Inverse Faulty System Model 5.3 Implicit System Inversion 5.4 Input Reconstruction from a Bicausal Bond Graph of the Inverse Faulty System Example: Input Reconstruction on the Bicausal BG of a DC Motor Drive 5.5 Passive Fault Tolerant Control by Means of an Overwhelming Controller 5.6 Summary References 6 Software Support 6.1 Model Development and Simulation of the Dynamic System Behaviour 6.2 Model-Based Control 6.2.1 Observability and Controllability 6.2.2 Design of a Luenberger Observer in Octave 6.2.3 Parameter Estimation and System Inversion on a Bicausal Bond Graph 6.3 Fault Diagnosis 6.3.1 Signal Preprocessing 6.3.2 State Estimation and Observer-Based Fault Detection 6.3.3 FDI Based on ARRs Derived from a DBG 6.3.4 Combined Bond Graph Model-Based Data-Driven Failure Prognosis Bond Graph Model-Based Generation of Discrete Degradation Data 6.4 Summary References 7 Applications 7.1 Introduction 7.2 Half-Wave Voltage Doubler 7.2.1 Modelling and Analysis of the Voltage Doubler A Switched LTI Model Implementation of the LTI System Simulation Results 7.2.2 Fault Diagnosis on the Voltage Doubler Fault Scenario 1: Exponential Decline of the Output Capacitance C̃2(t) as of a Time Instant t1 Fault Scenario 2: Open Circuit of Diode D1 7.3 Reconstruction of the Capacitance of a Leaking Electrolytic Capacitor 7.3.1 Estimation of the Decaying Capacitance Based on a Bicausal BG 7.3.2 ARR-Based Estimation of the Capacitance Degradation Values 7.3.3 RUL Prediction 7.4 External Leakage from a Closed Loop Three Tanks System 7.4.1 Modelling and Analysis of the System Open Loop System Closed Loop System Fault Scenario: Hole of Increasing Size in the Bottom of Tank 3 7.4.2 RUL Estimation 7.5 Fault Signature Matrix of a Hydraulic Actuator with Leakage 7.6 Internal Friction in a Permanent Magnet DC Motor 7.6.1 Modelling of the DC Motor Drive 7.6.2 Fault Detection 7.6.3 Fault Scenario: Friction in the DC Motor Increases Linearly as of a Time Instant 7.6.4 RUL Estimation 7.7 Fault Accommodation in an Open Loop DC Motor Drive 7.7.1 Fault Scenario 1: Increase in the Motor Armature Resistance Analytical Determination of Steady State Values Simulation of the Recovery from the Fault 7.7.2 Fault Scenario 2: Leakage in the Buck Converter Capacitor Determination of a New System Input Analytical Determination of Steady State Values Simulation of the Recovery from the Fault 7.8 Robust Overwhelming Control of a Mechanical Oscillator Simulation of the Closed Loop Oscillator 7.9 Summary References 8 Conclusions Model-Based Control Fault Detection and Isolation Failure Prognosis Fault Tolerant Control Some Subjects of Further Work References A Some Definitions A.1 Fault Diagnosis A.2 Failure Prognostic References B Short Introduction into Bond Graph Modelling B.1 Basic Concepts B.1.1 Power Variables and Energy Variables B.1.2 Analogies B.1.3 Hierarchical Bond Graph Models B.2 Bond Graph Elements B.2.1 Supply and Absorption of Energy B.2.2 Energy Storage B.2.3 Irreversible Transformation of Energy into Heat B.2.4 Reversible Transformation of Energy B.2.5 Power Conservative Distribution of Energy B.3 Systematic Construction of Acausal Bond Graphs B.3.1 Mechanical Subsystems (Translation and Fixed-Axis Rotation) B.3.2 Non-mechanical Subsystems B.3.3 Assignment of Power Reference Directions B.4 The Concept of Computational Causality at Power Ports B.4.1 Rules for Computational Causalities at Power Ports B.4.2 Sequential Assignment of Computational Causalities Sequential Causality Assignment Procedure (SCAP) B.5 Derivation of Equations from Causal Bond Graphs B.5.1 Procedure for Manually Deducing Equations from a Causal Bond Graph B.5.2 A Circuit with an Operational Amplifier B.5.3 A Switched Circuit B.6 Characteristic Bond Graph Features in a Nutshell B.7 Bond Graphs: A Core Model Representation B.8 Summary References C Some Mathematical Background C.1 A Lyapunov Function C.2 LaSalle's Invariance Principle C.3 Implicit Function Theorem C.4 Inverse Model of Non-reduced Order References Glossary References Index