CONTENTS
1 Introduction = 1
1.1 Necessity for the Fault Diagnosis and Condition Monitoring of Liquid Rocket Engine = 1
1.2 History and Development of LRE Fault Diagnostics Technology = 4
1.3 Development Trend of the LRE Fault Diagnosis = 8
2 Failure Pattern and Corresponding Mechanism Analysis of LRE = 11
2.1 Introduction = 11
2.2 Structure of LRE = 12
2.3 Failure Pattern Analysis of the LRE = 12
2.4 Failure Mechanism Analysis of the LRE = 14
2.4.1 Thrust Chamber and Gas Generator = 14
2.4.2 Turbo Pump = 17
2.4.3 Seal Components = 26
2.5 Standard Failure Pattern of the LRE = 28
3 Analysis Methods of Failure Model for LRE = 37
3.1 Introduction = 37
3.2 Working Process of LRE = 38
3.3 Model of Steady State Process for LRE = 39
3.3.1 Analysis of Liquid Flow in the Pipeline = 39
3.3.2 Working Characteristic Equation of Engine Parts = 40
3.3.3 Parameter Balance Model of Engine = 46
3.3.4 Fault Characteristic Equation of Engine Components [55, 62–64] = 48
3.3.5 Steady State Model of the First-Stage Engine = 54
3.3.6 Steady State Model of the Second-Stage Engine = 54
3.4 Dynamic Model of LRE [55, 62–64] = 55
3.4.1 Thrust Chamber = 55
3.4.2 Gas Generator = 57
3.4.3 Turbine Pump System = 58
3.4.4 Liquid Pipeline System = 60
3.4.5 Autogenous Pressurization System = 64
3.4.6 Dynamic Model of the First-Stage Engine = 69
3.4.7 Dynamic Model of the Second-Stage Engine = 69
4 Fault Characteristic Analysis of LRE = 71
4.1 Characteristic Analysis of Failure Patterns in Steady State = 71
4.1.1 Numerical Solution of the Steady State Model = 72
4.1.2 Simulation Analysis of Steady State Feature = 73
4.1.3 The Numerical Method Based on the Hopfield Neural Nets = 75
4.1.4 Analysis of Engine Steady State Fault = 78
4.1.5 Secondary Engine Steady State Fault Analysis Based on Ant Colony Algorithm = 81
4.1.6 Analysis of Engine Fault Feature Based on Evolutionary Calculation = 85
4.1.7 Acquisition of Engine Steady State Failure Mode = 99
4.2 Analysis of Dynamic Failure Mode = 100
4.2.1 The Numerical Method for Solving the Dynamic Model of Engine = 101
4.2.2 Analysis of Engine Dynamic Fault = 102
4.3 Integrated Fault Analysis = 116
4.4 Separability and Detectability of Fault = 118
4.4.1 Separability of Fault = 118
4.4.2 The Detectability and Diagnostic Ability of Fault = 119
5 Fault Diagnosis of LRE Based on ANN = 121
5.1 Theory of ANN = 121
5.1.1 Basics of ANN = 122
5.1.2 BP ANN and Improved Algorithm = 123
5.2 Diagnostic Mechanism of ANN = 127
5.3 Fuzzy Preprocessing of the Input Data for the ANN = 129
5.4 Fault Diagnosis Method Based on BP ANN = 130
5.5 Fault Diagnosis Method Based on RBF ANN = 131
5.5.1 RBF ANN = 138
5.5.2 Application Examples = 142
5.5.3 Calculation Results and Analysis = 147
5.6 Fault Diagnosis Method Based on Improved ART2 ANN = 147
5.6.1 Selection of no Mentor Learning ANN Model = 148
5.6.2 Basic Structure and Theory of ART ANN = 150
5.6.3 Improved ART2 Algorithm = 153
5.6.4 Implementation of Improved ART2 Algorithm = 155
5.6.5 Fault Diagnosis Examples = 157
5.7 FTART ANN-based Fault Diagnosis Method = 158
5.7.1 FTART Structure and Basic Theory = 159
5.7.2 Improvement and Its Mathematical Description of FTART ANN = 160
5.7.3 Design of FTART ANN = 162
5.7.4 Diagnosis Examples and Analysis for FRART ANN = 164
6 Fault Diagnosis Method Based on Wavelet Transform = 165
6.1 Theory of Wavelet Transform = 165
6.1.1 Introduction = 165
6.1.2 Basic Theories of Wavelet Analysis [104] = 167
6.2 Fault Diagnosis Based on Wavelet Analysis for LRE = 176
6.2.1 Wavelet Packet Decomposition and Feature Extraction = 176
6.2.2 Time-Series Analysis Method and Its Application = 177
6.2.3 Harmonic Wavelet and Its Application = 181
6.2.4 Abnormal Vibration Monitoring and Diagnosis of Turbo Pump = 188
6.2.5 Sub-synchronous Precession Analysis of Turbo Pump Based on Wavelet Analysis = 189
6.2.6 Fault Diagnosis of LRE Based on Wavelet-ANN = 189
7 Fault Diagnosis Method Based on Artificial Immune System = 193
7.1 Artificial Immune System = 193
7.1.1 Natural Immune System = 193
7.2 Application of Negative Selection Principle to Fault Detection and Diagnosis of LRE = 199
7.2.1 Negative Selection Algorithm = 199
7.2.2 Case Study = 201
7.3 Application of the Clone Selection Principle in Start-up Progress Simulation of LRE = 209
7.3.1 Clone Selection Principle and Algorithm = 209
7.3.2 Case Study = 212
8 Fault Diagnosis Method Based on Fuzzy Theory = 219
8.1 Fuzzy Fault Diagnosis = 219
8.1.1 Basic Theory of the Fuzzy = 220
8.1.2 Fault Diagnosis Based on Fuzzy Theory = 221
8.2 Fault Diagnosis Method Based on Fuzzy Theory = 221
8.2.1 Basic Theory of the Fuzzy Pattern Recognition = 222
8.2.2 Template Method-Based Membership Function Construction and the FPR = 224
8.2.3 Multi-variable Membership Function and the FPR = 228
8.3 Fault Diagnosis Method Based on Fuzzy Clustering = 232
8.3.1 Dynamic Clustering Method Based on the Fuzzy Equivalence Matrix = 233
8.3.2 Clustering Method of Fuzzy ISODATA = 235
8.3.3 Fuzzy Clustering Based on Max— Transitivity and Its Application = 239
8.4 Fault Diagnosis Method Based on FNN = 241
8.4.1 Fuzzy Neural Network = 241
8.4.2 Fuzzy RBF ANN and Its Application in the Fault Diagnosis of LRE = 246
9 Fault Analysis and Diagnosis Method Based on Statistical Learning Theory = 255
9.1 Statistical Learning Theory and Support Vector Machine [136–140] = 255
9.1.1 Machine Learning = 257
9.1.2 Statistical Learning Theory = 258
9.1.3 Support Vector Machine = 260
9.1.4 Kernel Function and the Parameter Optimization = 262
9.2 Application of the SVM in the Fault Diagnosis of LRE = 266
9.2.1 Fault Character Analysis and Diagnosis of LRE in Steady State Based on SVM = 266
9.2.2 Fault Diagnosis of LRE Based on GA—SVM = 271
9.2.3 Fault Modeling and Analysis of LRE Based on SVM = 272
10 Fault Diagnosis Method Based on Hidden Markov Model = 279
10.1 Fault Diagnosis Method Based on HMM = 279
10.1.1 Basic Ideology of HMM = 280
10.1.2 Basic Algorithm of HMM = 283
10.1.3 The Type of HMM = 288
10.1.4 Improvement Measures of HMM in Practical Application = 289
10.1.5 A Pump Fault Diagnosis Turbo Based on HMM = 293
10.2 HMM-SVM Hybrid Fault Diagnosis Model and Its Application = 303
10.2.1 SVM Training = 304
10.2.2 HMM-SVM Fault Diagnosis Application Examples = 304
11 Fault Prediction Methods of Liquid Rocket Engine (LRE) = 307
11.1 Fault Prediction Method Based on Time Series Analysis = 308
11.1.1 Time Series Analysis = 308
11.1.2 Application and Analysis = 316
11.2 Fault Prediction Method Based on Gray Model = 317
11.2.1 Introduction of Gray Model = 317
11.2.2 Basic Mechanism of Gray Model = 317
11.2.3 Gray Prediction Method and Its Application in Fault Prediction of LRE = 318
11.3 Rocket Engine Fault Prediction Method Based on Neural Network = 327
11.3.1 Multistep Prediction Method for Dynamic Parameters of Rocket Engine Based on BP Network = 327
11.3.2 The Prediction Method for Dynamic of Rocket Engine Based on RBF Network = 331
11.3.3 The Prediction Method for Dynamic of Rocket Engine Based on Elman Network = 338
11.4 Rocket Engine Fault Prediction Based on SVM Method = 348
11.4.1 The Regression Estimation Based on Support Vector Machine = 349
11.4.2 Prediction Process and Evaluation Criteria Based on Support Vector Machine = 354
11.4.3 An Example of Liquid Rocket Engine Fault Prediction Based on SVM Method = 355
Appendix A : Steady State Fault Model of I-Level = 361
Appendix B : Steady State Fault Model of II-Level = 377
Appendix C : Dynamic State Fault Model of I-Level = 385
Appendix D : Dynamic State Fault Model of II-Level = 389
References = 395
