导语摘要 In this book, the complete theory systems and frameworks of aircrafttestability and fault prognosis techniques are established.The commontestability design analysis, fault diagnosis and prognosis methods of aircraftare introduced comprehensively. The objects of the book are to stand out thecharacteristics of aviation, to pursue comprehensive and detailed content, andto emphasize the combination of theory and application. In the aspect of theory, the advanced theories and techniques includingsignal analysis, multisignal modeling, test strategy optimization, sensor placement andsensing strategies,fault diagnosis methods, fault prognosis techniques and soon, which are involved in aircraft testability and fault prognosis, are allintroduced in detail. In this book, aircraft testability and fault prognosistechniques include: testability modeling and analysis for aircraft system faultdiagnosis and prognosis, vibration monitoring and fault diagnosis for aeroengine, trend analysis of aeroengine, andnondestructive testing of aircraft structure. In this book, many diagnosiscases are arranged in order to make theory to combine practice. The book can be acted as the teaching material for senior aircraftmaintenance majors, and reference book for postgraduate and technician of thismajor and related majors.
作者简介 苏艳：主要从事故障测试与维修排故、系统测试性设计分析、飞机结构适航测试等研究。
目录 Chapter 1 Introduction1 1.1 The significance of aircraft diagnosis and prognosis technology 1.2 Development of fault diagnosis and prognosis for aircraft system 1.3 Development of aircraft maintenance theory 1.3.1 Aircraft breakdown maintenance system 1.3.2 Aircraft hard time maintenance system 1.3.3 Aircraft reliability centered maintenance system 1.3.4 Aircraft on condition maintenance system 1.4 Condition monitoring and fault diagnosis techniques for aeroengie 1.4.1 Research objects 1.4.2 The basic theory 1.4.3 Condition monitoring and fault diagnosis system aero engine 1.5 Inspection and repair techniques for aircraft structure 1.5.1 Structure inspection and maintenance goals 1.5.2 Aircraft design service goal and economic service life 1.5.3 Aircraft structure integrity and aging aircraft structure maintenance
1.5.4 The aircraft nondestructive detection techniques 1.5.5 Aircraft leakage detection technology 1.6 Review questions Chapter 2 Testability Designing Analysis for System Diagnosis and Progosis 2.1 Introduction 2.2 Diagnostic and prognostic system requirements 2.3 Designing in fault diagnostic and prognostic systems 2.4 Diagnostic and prognostic functional layers 2.5 Testability modeling for complex system fault diagnosis 2.5.1 Types of failures 2.5.2 Designing completely testable systems using TEAMS 2.5.3 Testability design analysis based on MHFDG model for aircraft system ault diagnosis 2.6 Test strategy optimization method for aircraft system fault diagnosis 2.7 Review questions Chapter 3 Sensors and Sensing Strategies 3.1 Basic concepts of sensors, transducers and sensing strategies 3.2 Sensors application and quality requirements 3.2.1 Application 3.2.2 Quality requirements 3.3 The types of transducers 3.4 Type of sensors 3.4.1 Gases or liquids pressure sensors 3.4.2 Mechanical/structural sensor systems 3.4.3 Performance/operational sensors 3.4.4 Other new sensors 3.5 Sensor placement 3.6 Wireless sensor networks 3.7 Digital signal processing system 3.8 Review questions Chapter 4 Fault Signal Analysis and Processing 4.1 The concept and classification of signal 4.1.1 The concept of signal 4.1.2 The classification of signal 4.2 Time domain analysis 4.2.1 Amplitude domain 4.2.2 Time difference domain 4.2.3 Stationary stochastic process 4.2.4 Ergodic stochastic process 4.3 Frequency domain analysis 4.3.1 Impulse function and convolution 4.3.2 Fourier series 4.3.3 Fourier transform 4.4 Review questions Chapter 5 Theories for Fault Recognition and Diagnosis 5.1 Introduction 5.2 Fault diagnosis framework 5.2.1 Relevant definitions 5.2.2 Fault monitoring and diagnosis framework 5.3 Bayesian classification 5.3.1 Condition probability 5.3.2 Total probability formula 5.3.3 The Bayes decision based on minimum error ratio 5.3.4 Bayes decision based on the minimum average risk 5.4 Classification based on distance functions 5.4.1 Space distance (geometric distance) functions 5.4.2 Discriminant method with information distance 5.5 Fuzzy diagnosis 5.5.1 Membership function 5.5.2 Fuzzy vector 5.5.3 Fuzzy relationship 5.6 Grey diagnosis 5.6.1 Grey system modeling 5.6.2 The grey relation grade analysis 5.7 Neural network diagnosis 5.7.1 The topology structure and learning rules for artificial neural netwrk 5.7.2 Multlayer feed forward neural networks model and BP algorithm 5.8 Support vector machines diagnosis 5.8.1 Fundamental problems and methods of the machine learning 5.8.2 The core contents of statistical learning theory 5.8.3 Support vector machine 5.9 Expert system diagnosis 5.9.1 Introduction 5.9.2 Traditional rule based expert system principle 5.9.3 Diagnosis principle of neural network expert system 5.10 Model based fault diagnosis 5.10.1 Common fault modeling 5.10.2 Dynamic systems modeling 5.11 Data based fault diagnosis 5.11.1 Alarm bounds method 5.11.2 Statistical clustering methods 5.11.3 Neural network classification and clustering 5.12 Review questions Chapter 6 Prognosis Approaches for Aircrafts System and Compnent 6.1 Introduction 6.1.1 Revolutionary concepts made possible by prognostics 6.1.2 Prognostic applications related to aircraft 6.2 Prognostics approaches used in the aeronautical science164 6.2.1 Reliability based approach 6.2.2 Physics/model based approach 6.2.3 Data driven approach 6.2.4 Hybrid/fusion approach 6.3 Physics/model based fault prognosis 6.3.1 The state estimation 6.3.2 The RUL prediction 6.3.3 A case：pneumatic valves leakage prediction in refuelling systm 6.4 Data driven performance prognosis 6.4.1Data driven approach based on feed forward NN 6.4.2 Data driven prognostics based on Dynamic Wavelet Neural Netwrks (DWNNs) 6.4.3 A case: gas turbine performance prognosis 6.5 Time series prediction methods 6.5.1 Linear time series prediction methods 6.5.2 Nonlinear time series prediction methods 6.5.3 Time series prediction based on neural networks 6.5.4 The prediction method using support vector machine 6.6 Review questions Chapter 7 Condition Monitoring and Fault Diagnosis Techniques for Aeroegine1 7.1 Aeroengine state diagnosis 7.1.1 Basic principle of engine state diagnosis 7.1.2 Basic concepts for fault diagnosis 7.1.3 Fault equation 7.1.4 Conclusion
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