Ganguli Gas Turbine Diagnostics

Signal Processing and Fault Isolation
1. Auflage 2013
ISBN: 978-1-4665-0281-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 251 Seiten

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.

Zielgruppe

Engineers working on gas turbines in aircraft engine companies, power companies, and technical divisions of airlines; researchers working on gas turbine diagnostics, fault isolation of systems, fuzzy logic, soft computing, signal processing, and algorithms; students and professors of mechanical, aerospace, and electrical engineering, computer science, and applied mathematics.

Autoren/Hrsg.

Ganguli, Ranjan

Fachgebiete

• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Triebwerkstechnik, Energieübertragung

Inhaltsverzeichnis

Introduction
Background
Signal Processing
Typical Gas Turbine Diagnostics
Linear Filters
Median Filters
Least-Squares Approach
Kalman Filter
Influence Coefficients
Vibration-Based Diagnostics

Idempotent Median Filter
Weighted Median Filter
Center Weighted Median Filter
Center Weighted Idempotent Median Filter
Test Signal
Error Measure
Summary

Median-Rational Hybrid Filters
Test Signals
Rational Filter
Median-Rational Filter
Numerical Simulations
Summary

FIR-Median Hybrid Filters
FIR-Median Hybrid (FMH) Filters
Weighted FMH Filter
Test Signal
Numerical Simulations
Summary

Transient Data and the Myriad Filter
Steady-State and Transient Signals
Myriad Filter
Numerical Simulations
Gas Turbine Transient Signal
Weighted Myriad Algorithm
Adaptive Weighted Myriad Filter Algorithm
Summary

Trend Shift Detection
Problem Formulation
Image Processing Concepts
Median Filter
Recursive Median Filter
Cascaded Recursive Median Filter
Edge Detection
Numerical Simulations
Trend Shift Detection
Summary

Optimally Weighted Recursive Median Filters
Weighted Recursive Median Filters
Test Signals
Numerical Simulations
Test Signal with Outliers
Performance Comparison
Three- and Seven-Point Optimally Weighted RM Filters
Simulations

Kalman Filter
Kalman Filter Approach
Single-Fault Isolation
Numerical Simulations
Sensor Error Compensation
Summary

Neural Network Architecture
Artificial Neural Network Approach
Kalman Filter and Neural Network Methods
Autoassociative Neural Network
Summary

Fuzzy Logic System
Module and System Faults
Fuzzy Logic System
Defuzzification
Problem Formulation
Fuzzification
Rules and Fault Isolation
Numerical Simulations
Summary

Soft Computing Approach
Gas Turbine Fault Isolation
Neural Signal Processing—Radial Basis Function Neural Networks
Fuzzy Logic System
Genetic Algorithm
Genetic Fuzzy System
Numerical Simulations
Summary

Vibration-Based Diagnostics
Formulations
Numerical Simulations
Summary

References

Index

Über Autor(innen)

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.