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.
Table of Contents
Typical Gas Turbine Diagnostics
Idempotent Median Filter
Weighted Median Filter
Center Weighted Median Filter
Center Weighted Idempotent Median Filter
Median-Rational Hybrid Filters
FIR-Median Hybrid Filters
FIR-Median Hybrid (FMH) Filters
Weighted FMH Filter
Transient Data and the Myriad Filter
Steady-State and Transient Signals
Gas Turbine Transient Signal
Weighted Myriad Algorithm
Adaptive Weighted Myriad Filter Algorithm
Trend Shift Detection
Image Processing Concepts
Recursive Median Filter
Cascaded Recursive Median Filter
Trend Shift Detection
Optimally Weighted Recursive Median Filters
Weighted Recursive Median Filters
Test Signal with Outliers
Three- and Seven-Point Optimally Weighted RM Filters
Kalman Filter Approach
Sensor Error Compensation
Neural Network Architecture
Artificial Neural Network Approach
Kalman Filter and Neural Network Methods
Autoassociative Neural Network
Fuzzy Logic System
Module and System Faults
Fuzzy Logic System
Rules and Fault Isolation
Soft Computing Approach
Gas Turbine Fault Isolation
Neural Signal Processing—Radial Basis Function Neural Networks
Fuzzy Logic System
Genetic Fuzzy System
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.
"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
"Today’s 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 Ganguli’s 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