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E-grāmata: Gas Turbine Diagnostics: Signal Processing and Fault Isolation

5.00/5 (2 ratings by Goodreads)
(Viasat Inc., Phoenix, USA)
  • Formāts: 251 pages
  • Izdošanas datums: 13-Dec-2012
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781466502819
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Gas Turbine Diagnostics: Signal Processing and Fault IsolationPalielināt
  • Formāts: 251 pages
  • Izdošanas datums: 13-Dec-2012
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781466502819
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"A discussion of the different methods in data filtering, trend shift detection, and fault isolation developed over the past decade, this book provides a variety of new research tools for use in the condition monitoring of jet engines. Each method is demonstrated through numerical simulations which can be easily done using worksheets such as MS Excel or through Matlab. The algorithms presented will be useful to engineers and scientists working on fault diagnosis of gas turbine engines. The data cleaning algorithms based on nonlinear signal processing provided are also applicable to condition and health monitoring problems in general. "--

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 methods

Using 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.

Recenzijas

"Very well written and easy to understand for practical use by engineers in industry and researchers from academia and industry. ... Excellent book on the topic with comprehensive description of the theory and a simple approach for gas turbine engine performance diagnostics."

Ashwani K. Gupta, University of Maryland, College Park, USA

"... unique ... a single reference for numerous techniques of fault analysis and isolation. The book in its 12 chapters provides an organized way for fault analysis in gas turbines. Simple algorithms using MATLAB® are developed based on Kalman filters, neural networks and fuzzy logic, and a hybrid soft computing approach. The book is useful for both engineers and scientists interested in gas turbine diagnostics."

Dr. Ahmed F. El-Sayed, Zagazig University, Egypt

"The book provides a good overview of the subject of signal processing and fault isolation. The book is well structured, with individual chapters providing a good overview of a specific aspect of the subject. The book would make a good reference text for a more experienced engineer, and also assist those new to the subject to learn about specific signal processing and fault isolation techniques." Anthony Geoffrey Sheard, Flakt Woods Limited, UK

"Todays gas turbine industry is a multi-billion dollar business. ... The use of advanced simulation and analysis has been gaining importance, particularly in the area of gas path diagnostics. Whilst academia has made important contributions, it is industry that has to employ these advanced techniques. Professor Ranjan Gangulis book, Gas Turbine Diagnostics: Signal Processing and Fault Isolation, is an important contribution because of the blend of scholarship and industry practice." Professor Riti Singh, Cranfield University, UK "Very well written and easy to understand for practical use by engineers in industry and researchers from academia and industry. ... Excellent book on the topic with comprehensive description of the theory and a simple approach for gas turbine engine performance diagnostics."Ashwani K. Gupta, University of Maryland, College Park, USA

"... unique ... a single reference for numerous techniques of fault analysis and isolation. The book in its 12 chapters provides an organized way for fault analysis in gas turbines. Simple algorithms using MATLAB® are developed based on Kalman filters, neural networks and fuzzy logic, and a hybrid soft computing approach. The book is useful for both engineers and scientists interested in gas turbine diagnostics."Dr. Ahmed F. El-Sayed, Zagazig University, Egypt

"The book provides a good overview of the subject of signal processing and fault isolation. The book is well structured, with individual chapters providing a good overview of a specific aspect of the subject. The book would make a good reference text for a more experienced engineer, and also assist those new to the subject to learn about specific signal processing and fault isolation techniques."Anthony Geoffrey Sheard, Flakt Woods Limited, UK

"Todays gas turbine industry is a multi-billion dollar business. ... The use of advanced simulation and analysis has been gaining importance, particularly in the area of gas path diagnostics. Whilst academia has made important contributions, it is industry that has to employ these advanced techniques. Professor Ranjan Gangulis book, Gas Turbine Diagnostics: Signal Processing and Fault Isolation, is an important contribution because of the blend of scholarship and industry practice."Professor Riti Singh, Cranfield University, UK

Preface ix
About the Author xi
1 Introduction
1(18)
1.1 Background
1(2)
1.2 Signal Processing
3(2)
1.3 Typical Gas Turbine Diagnostics
5(2)
1.4 Linear Filters
7(1)
1.5 Median Filters
7(2)
1.6 Least-Squares Approach
9(3)
1.7 Kalman Filter
12(2)
1.8 Influence Coefficients
14(3)
1.9 Vibration-Based Diagnostics
17(2)
2 Idempotent Median Filters
19(14)
2.1 Weighted Median Filter
19(1)
2.2 Center Weighted Median Filter
20(1)
2.3 Center Weighted Idempotent Median Filter
21(1)
2.3.1 Filter Design for Gas Path Measurements
21(1)
2.4 Test Signal
22(6)
2.4.1 Ideal Signal
23(1)
2.4.2 Noisy Signal
23(5)
2.5 Error Measure
28(3)
2.5.1 Numerical Simulations
28(3)
2.6 Summary
31(2)
3 Median-Rational Hybrid Filters
33(10)
3.1 Test Signals
33(4)
3.2 Rational Filter
37(1)
3.3 Median-Rational Filter
38(2)
3.4 Numerical Simulations
40(1)
3.5 Summary
41(2)
4 FIR-Median Hybrid Filters
43(10)
4.1 FIR-Median Hybrid (FMH) Filters
43(1)
4.2 Weighted FMH Filter
44(1)
4.3 Test Signal
45(3)
4.3.1 Root Signal
46(1)
4.3.2 Gaussian Noise
47(1)
4.3.3 Outliers
47(1)
4.3.4 Error Measure
47(1)
4.4 Numerical Simulations
48(3)
4.5 Summary
51(2)
5 Transient Data and the Myriad Filter
53(22)
5.1 Steady-State and Transient Signals
53(1)
5.2 Myriad Filter
54(2)
5.3 Numerical Simulations
56(3)
5.4 Gas Turbine Transient Signal
59(1)
5.5 Weighted Myriad Algorithm
59(7)
5.6 Adaptive Weighted Myriad Filter Algorithm
66(4)
5.7 Numerical Simulations
70(2)
5.8 Summary
72(3)
6 Trend Shift Detection
75(18)
6.1 Problem Formulation
76(1)
6.2 Image Processing Concepts
77(1)
6.3 Median Filter
77(1)
6.4 Recursive Median Filter
78(1)
6.5 Cascaded Recursive Median Filter
79(1)
6.6 Edge Detection
80(1)
6.6.1 Gradient Edge Detector
80(1)
6.6.2 Laplacian Edge Detector
80(1)
6.7 Numerical Simulations
81(4)
6.7.1 Test Signal
81(2)
6.7.2 Noise Reduction
83(1)
6.7.3 Outlier Removal
84(1)
6.8 Trend Shift Detection
85(6)
6.8.1 Threshold Selection
87(3)
6.8.2 Testing of Trend Detection Algorithm
90(1)
6.9 Summary
91(2)
7 Optimally Weighted Recursive Median Filters
93(32)
7.1 Weighted Recursive Median Filters
94(1)
7.2 Test Signals
94(4)
7.3 Numerical Simulations
98(5)
7.4 Test Signal with Outliers
103(4)
7.5 Performance Comparison
107(3)
7.6 Three- and Seven-Point Optimally Weighted RM Filters
110(13)
7.6.1 Numerical Analysis
110(3)
7.6.2 Signal with Outliers
113(10)
7.7 Summary
123(2)
8 Kalman Filter
125(16)
8.1 Kalman Filter Approach
125(3)
8.2 Single-Fault Isolation
128(5)
8.3 Numerical Simulations
133(2)
8.4 Sensor Error Compensation
135(4)
8.5 Summary
139(2)
9 Neural Network Architecture
141(10)
9.1 Artificial Neural Network Approach
141(5)
9.1.1 Back-Propagation (BP) Algorithm
142(3)
9.1.2 Hybrid Neural Network Algorithm
145(1)
9.2 Kalman Filter and Neural Network Methods
146(1)
9.3 Autoassociative Neural Network
147(1)
9.4 Summary
148(3)
10 Fuzzy Logic System
151(18)
10.1 Module and System Faults
151(1)
10.2 Fuzzy Logic System
152(4)
10.3 Defuzzification
156(1)
10.4 Problem Formulation
156(1)
10.4.1 Input and Output
156(1)
10.5 Fuzzification
157(3)
10.6 Rules and Fault Isolation
160(1)
10.7 Numerical Simulations
161(6)
10.8 Summary
167(2)
11 Soft Computing Approach
169(20)
11.1 Gas Turbine Fault Isolation
170(1)
11.2 Neural Signal Processing---Radial Basis Function Neural Networks
170(1)
11.3 Fuzzy Logic System
171(1)
11.4 Genetic Algorithm
172(2)
11.5 Genetic Fuzzy System
174(2)
11.6 Numerical Simulations
176(10)
11.7 Summary
186(3)
12 Vibration-Based Diagnostics
189(24)
12.1 Formulations
191(8)
12.1.1 Modeling of Turbine Blade
191(2)
12.1.2 Fatigue Damage Model
193(6)
12.1.3 Beam with Fatigue Damage
199(1)
12.2 Numerical Simulations
199(11)
12.2.1 Finite Element Simulations
200(1)
12.2.2 Damage Detection
201(9)
12.3 Summary
210(3)
References 213(8)
Index 221
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.
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