Model-Aided Diagnosis of Mechanical Systems, Softcover reprint of the original 1st ed. 1997 Fundamentals, Detection, Localization, Assessment

Diagnosis of technical systems is important, concerning safety and economics. Monitoring and diagnosis, especially in remote control systems, needs holistic models, as described here. This book describes the fundamentals for technical diagnosis as well as state-of-art tools. Model-based diagnosis and knowledge based diagnosis, fundamentals in decision-making and assessment are included. The foundation of diagnosis for applications is given. The book is written for the expert practising engineer in research and industrial applications.

1 Introduction.- 1.1 The Intention.- 1.2 The Concept.- 1.3 Modelling.- 1.3.1 Modelling of Systems.- 1.3.2 Modelling of Uncertainties.- 1.3.3 Holistic Modelling.- 1.4 Summary.- 2 Review of Tools and Concepts of Diagnosis.- 2.1 Damage, Faults and their Descriptions.- 2.1.1 General Discussion.- 2.1.2 Parameter Modifications: Preliminary Examples.- 2.1.3 Parameter Modifications: General Analysis.- 2.1.4 Model Structure Modifications.- 2.2 Symptoms.- 2.2.1 Introductory Remarks: Symptoms, Discriminants, Features.- 2.2.2 Symptoms of Linear Systems.- 2.2.3 Symptoms of Non-linear Systems.- 2.2.4 Features and Patterns, Classification.- 2.2.5 Remarks on the Decision Problem.- 2.2.6 Remarks on the Localization Problem.- 2.3 Damage Initiation and Evolution in Operating Systems.- 2.3.1 The Physics of Structural Wear and Damage.- 2.3.2 The Energy Model of System Damage and its Measure.- 2.3.3 Damage and Symptom Evolution.- 2.3.4 Mechanical Properties of Operating Systems in the Holistic Approach.- 2.3.5 Damage Capacity and Symptom Reliability of Operating Systems and their Diagnostic Use.- 2.3.6 Application of the Damage Evolution Theory.- 2.4 Summary.- 3 Fault-Related Processes: Monitoring, Measurements, Processing of Signals.- 3.1 Monitoring.- 3.1.1 The Goal of Condition Monitoring.- 3.1.2 Symptoms, Processes and their Characteristics.- 3.2 Measurements.- 3.2.1 The Goal of Measurements.- 3.2.2 Environmental and Boundary Conditions.- 3.2.3 Sensors: Properties, Calibration, Measuring Errors, Locations.- 3.2.4 Test/Measurement Set-up.- 3.3 Signal Pre-processing.- 3.3.1 Conditioning, Sampling, Pre-analysis.- 3.3.2 Filtering and Segmentation.- 3.4 Signal Processing.- 3.4.1 Increase of the Signal-to-noise Ratio.- 3.4.2 Information Condensation.- 3.4.3 Inverse Filtering.- 3.5 Summary.- 4 Model-Supported Diagnostics Via Parameter Estimation.- 4.1 The General Procedure.- 4.2 Model Adjustment - Preparations.- 4.2.1 Models.- 4.2.2 Tests.- 4.2.3 Residuals.- 4.2.4 Estimators.- 4.3 Model Adjustment - Methods.- 4.3.1 Subsystem Modelling.- 4.3.2 Adjustment Based on Eigenvalues.- 4.3.3 Adjustment with Identified Eigenvalues and Eigenvectors.- 4.3.4 Adjustment Based on Input/Output Measurements.- 4.3.5 State Observers.- 4.3.6 ARMA Models.- 4.3.7 Additional Methods.- 4.4 Algorithms.- 4.4.1 Linear Equation Solvers.- 4.4.2 Nonlinear Equation Solvers.- 4.4.3 Role of Regularization.- 4.5 Practical Hints.- 4.5.1 General.- 4.5.2 Incomplete Measurements.- 4.5.3 The Use of Order-Reduced Models.- 4.6 Use of the Knowledge Base for Diagnosis.- 4.6.1 Parameter Errors: Covariance Matrix and Confidence Regions.- 4.6.2 Dynamic Models.- 4.6.3 Static Models.- 4.6.4 Trend.- 4.7 Summary.- 5 Decision, Assessment, and System Modification Procedures.- 5.1 Fundamentals of Decision.- 5.1.1 Pseudo-deterministic Decision.- 5.1.2 Bayesian Decision Theory.- 5.1.3 Neyman-Pearson Rule.- 5.1.4 Fuzzy Logic and Decision.- 5.2 Symptom-based Decision.- 5.2.1 Deterministic Decision.- 5.2.2 Statistical Decision Based on Histograms.- 5.2.3 Statistical Decision Using Significance Tests.- 5.3 Model-based Decision.- 5.4 Assessment.- 5.4.1 Assessment of State Condition by Simulation.- 5.4.2 Cause Finding.- 5.4.3 Trend Prediction.- 5.4.4 Actions.- 5.5 System Modification Procedures.- 5.5.1 Sensitivity Analysis.- 5.5.2 Spectral Decomposition.- 5.5.3 Minimum Modification by the Application of Optimization.- 5.6 Summary.- 6 Examples and Remarks on Applications.- 6.1 An Academic Example: The FE-Model of a Satellite.- 6.1.1 The System and its Model.- 6.1.2 The Damaged System and the Simulation of the Dynamic Response.- 6.1.3 The Localization of Faults.- 6.1.4 The Adjusted Mathematical Model.- 6.2 Application in Mechanical Engineering.- 6.2.1 The Choice of Symptom Operator Shape.- 6.2.2 Model-Based Diagnostics for Rotor Machinery Including Turbosets.- 6.3 Application in Civil Engineering: Norderelb Bridge.- 6.3.1 The System and its Model.- 6.3.2 Identification of Eigenquantities.- 6.3.3 Comparison of Calculated and Estimated Eigenquantities.- 6.3.4 Simulated Damage.- 6.4 Conclusion.- 7 Concluding Remarks.- References.

Diagnosis of technical systems is important, concerning safety and economics. Monitoring and diagnosis, especially in remote control systems, needs holistic models, as described here. This book presents the fundamentals for technical diagnosis as well as state-of-the-art tools. Model-based diagnosis and knowledge based diagnosis, fundamentals in decision-making and assessment are included. The foundation of diagnosis for applications is given. The book is written for the expert practising engineer in research and industrial applications.