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Summary
Summary
Widely used for power generation, gas turbine engines are susceptible to faults due to the harsh working environment. Most engine problems are preceded by a sharp change in measurement deviations compared to a baseline engine, but the trend data of these deviations over time are contaminated with noise and non-Gaussian outliers. Gas Turbine Diagnostics: Signal Processing and Fault Isolation presents signal processing algorithms to improve fault diagnosis in gas turbine engines, particularly jet engines. The algorithms focus on removing noise and outliers while keeping the key signal features that may indicate a fault.
The book brings together recent methods in data filtering, trend shift detection, and fault isolation, including several novel approaches proposed by the author. Each method is demonstrated through numerical simulations that can be easily performed by the reader. Coverage includes:
Filters for gas turbines with slow data availability Hybrid filters for engines equipped with faster data monitoring systems Nonlinear myriad filters for cases where monitoring of transient data can lead to better fault detection Innovative nonlinear filters for data cleaning developed using optimization methods An edge detector based on gradient and Laplacian calculations A process of automating fault isolation using a bank of Kalman filters, fuzzy logic systems, neural networks, and genetic fuzzy systems when an engine model is available An example of vibration-based diagnostics for turbine blades to complement the performance-based methodsUsing simple examples, the book describes new research tools to more effectively isolate faults in gas turbine engines. These algorithms may also be useful for condition and health monitoring in other systems where sharp changes in measurement data indicate the onset of a fault.
Author Notes
Dr. Ranjan Ganguli is a professor in the Aerospace Engineering Department of the Indian Institute of Science (IISc), Bangalore. He received his MS and Ph.D. degrees from the Department of Aerospace Engineering at the University of Maryland, College Park, and his B.Tech. degree in aerospace engineering from the Indian Institute of Technology. He has worked at Pratt & Whitney on engine gas path diagnostics and, during his academic career at IISc, has conducted sponsored research projects for companies such as Boeing, Pratt & Whitney, Honeywell, and HAL. He has authored or coauthored three books, published more than 140 papers in refereed journals, and presented more than 80 papers at conferences. He is a fellow of the American Society of Mechanical Engineers, the Royal Aeronautical Society, and the Indian National Academy of Engineering, and an associate fellow of the American Institute of Aeronautics and Astronautics. He received the Alexander von Humboldt Fellowship and the Fulbright Fellowship in 2007 and 2011, respectively. He is an associate editor of the AIAA Journal and the Journal of the American Helicopter Society.
Table of Contents
Preface | p. ix |
About the Author | p. xi |
1 Introduction | p. 1 |
1.1 Background | p. 1 |
1.2 Signal Processing | p. 3 |
1.3 Typical Gas Turbine Diagnostics | p. 5 |
1.4 Linear Filters | p. 7 |
1.5 Median Filters | p. 7 |
1.6 Least-Squares Approach | p. 9 |
1.7 Kalman Filter | p. 12 |
1.8 Influence Coefficients | p. 14 |
1.9 Vibration-Based Diagnostics | p. 17 |
2 Idempotent Median Filters | p. 19 |
2.1 Weighted Median Filter | p. 19 |
2.2 Center Weighted Median Filter | p. 20 |
2.3 Center Weighted Idempotent Median Filter | p. 21 |
2.3.1 Filter Design for Gas Path Measurements | p. 21 |
2.4 Test Signal | p. 22 |
2.4.1 Ideal Signal | p. 23 |
2.4.2 Noisy Signal | p. 23 |
2.5 Error Measure | p. 28 |
2.5.1 Numerical Simulations | p. 28 |
2.6 Summary | p. 31 |
3 Median-Rational Hybrid Filters | p. 33 |
3.1 Test Signals | p. 33 |
3.2 Rational Filter | p. 37 |
3.3 Median-Rational Filter | p. 38 |
3.4 Numerical Simulations | p. 40 |
3.5 Summary | p. 41 |
4 FIR-Median Hybrid Filters | p. 43 |
4.1 FIR-Median Hybrid (FMH) Filters | p. 43 |
4.2 Weighted FMH Filter | p. 44 |
4.3 Test Signal | p. 45 |
4.3.1 Root Signal | p. 46 |
4.3.2 Gaussian Noise | p. 47 |
4.3.3 Outliers | p. 47 |
4.3.4 Error Measure | p. 47 |
4.4 Numerical Simulations | p. 48 |
4.5 Summary | p. 51 |
5 Transient Data and the Myriad Filter | p. 53 |
5.1 Steady-State and Transient Signals | p. 53 |
5.2 Myriad Filter | p. 54 |
5.3 Numerical Simulations | p. 56 |
5.4 Gas Turbine Transient Signal | p. 59 |
5.5 Weighted Myriad Algorithm | p. 59 |
5.6 Adaptive Weighted Myriad Filter Algorithm | p. 66 |
5.7 Numerical Simulations | p. 70 |
5.8 Summary | p. 72 |
6 Trend Shift Detection | p. 75 |
6.1 Problem Formulation | p. 76 |
6.2 Image Processing Concepts | p. 77 |
6.3 Median Filter | p. 77 |
6.4 Recursive Median Filter | p. 78 |
6.5 Cascaded Recursive Median Filter | p. 79 |
6.6 Edge Detection | p. 80 |
6.6.1 Gradient Edge Detector | p. 80 |
6.6.2 Laplacian Edge Detector | p. 80 |
6.7 Numerical Simulations | p. 81 |
6.7.1 Test Signal | p. 81 |
6.7.2 Noise Reduction | p. 83 |
6.7.3 Outlier Removal | p. 84 |
6.8 Trend Shift Detection | p. 85 |
6.8.1 Threshold Selection | p. 87 |
6.8.2 Testing of Trend Detection Algorithm | p. 90 |
6.9 Summary | p. 91 |
7 Optimally Weighted Recursive Median Filters | p. 93 |
7.1 Weighted Recursive Median Filters | p. 94 |
7.2 Test Signals | p. 94 |
7.3 Numerical Simulations | p. 98 |
7.4 Test Signal with Outliers | p. 103 |
7.5 Performance Comparison | p. 107 |
7.6 Three- and Seven-Point Optimally Weighted RM Filters | p. 110 |
7.6.1 Numerical Analysis | p. 110 |
7.6.2 Signal with Outliers | p. 113 |
7.7 Summary | p. 123 |
8 Kalman Filter | p. 125 |
8.1 Kalman Filter Approach | p. 125 |
8.2 Single-Fault Isolation | p. 128 |
8.3 Numerical Simulations | p. 133 |
8.4 Sensor Error Compensation | p. 135 |
8.5 Summary | p. 139 |
9 Neural Network Architecture | p. 141 |
9.1 Artificial Neural Network Approach | p. 141 |
9.1.1 Back-Propagation (BP) Algorithm | p. 142 |
9.1.2 Hybrid Neural Network Algorithm | p. 145 |
9.2 Kalman Filter and Neural Network Methods | p. 146 |
9.3 Autoassociative Neural Network | p. 147 |
9.4 Summary | p. 148 |
10 Fuzzy Logic System | p. 151 |
10.1 Module and System Faults | p. 151 |
10.2 Fuzzy Logic System | p. 152 |
10.3 Defuzzification | p. 156 |
10.4 Problem Formulation | p. 156 |
10.4.1 Input and Output | p. 156 |
10.5 Fuzzification | p. 157 |
10.6 Rules and Fault Isolation | p. 160 |
10.7 Numerical Simulations | p. 161 |
10.8 Summary | p. 167 |
11 Soft Computing Approach | p. 169 |
11.1 Gas Turbine Fault Isolation | p. 170 |
11.2 Neural Signal Processing--Radial Basis Function Neural Networks | p. 170 |
11.3 Fuzzy Logic System | p. 171 |
11.4 Genetic Algorithm | p. 172 |
11.5 Genetic Fuzzy System | p. 174 |
11.6 Numerical Simulations | p. 176 |
11.7 Summary | p. 186 |
12 Vibration-Based Diagnostics | p. 189 |
12.1 Formulations | p. 191 |
12.1.1 Modeling of Turbine Blade | p. 191 |
12.1.2 Fatigue Damage Model | p. 193 |
12.1.3 Beam with Fatigue Damage | p. 199 |
12.2 Numerical Simulations | p. 199 |
12.2.1 Finite Element Simulations | p. 200 |
12.2.2 Damage Detection | p. 201 |
12.3 Summary | p. 210 |
References | p. 213 |
Index | p. 221 |