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Author: Dan M. Ghiocel Publisher: ISBN: Category : Languages : en Pages : 10
Book Description
One key aspect when developing a real-time in-flight risk-based health management system for jet engines is the development of accurate and robust fault classifiers. Regardless of the complex uncertainty propagation in the data fusion process, the selection of fault classifiers is the critical aspect of a health management system. The paper illustrates the application of a hybrid Stochastic-Fuzzy-Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both the engine performance. The random fluctuations of jet engine performance parameters during flight missions are modeled using multivariate stochastic models. The fault diagnostic and prognostic risks are computed using a stochastic model-based deviation (using a gas-path analysis model) approach. At any time the engine operation for the future is approached as a conditional reliability problem where the conditional data are represented by the past operational history monitored on-line by the engine health management (ERM) system. To capture the complex functional relationships between different engine performance parameters during flight fast an adaptive network-based fuzzy inference system is employed. This increases significantly the robustness of the ERM system during highly transient in-flight conditions. Both the monitored and fault data uncertainties are considered in a multidimensional parameter space, with two probabilistic-based safety margins employed for fault detection and diagnostic, as follows: (i) Anomaly Detection Margin (ADM) and (ii) Fault Detection Margin (FDM).
Author: Dan M. Ghiocel Publisher: ISBN: Category : Languages : en Pages : 10
Book Description
One key aspect when developing a real-time in-flight risk-based health management system for jet engines is the development of accurate and robust fault classifiers. Regardless of the complex uncertainty propagation in the data fusion process, the selection of fault classifiers is the critical aspect of a health management system. The paper illustrates the application of a hybrid Stochastic-Fuzzy-Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both the engine performance. The random fluctuations of jet engine performance parameters during flight missions are modeled using multivariate stochastic models. The fault diagnostic and prognostic risks are computed using a stochastic model-based deviation (using a gas-path analysis model) approach. At any time the engine operation for the future is approached as a conditional reliability problem where the conditional data are represented by the past operational history monitored on-line by the engine health management (ERM) system. To capture the complex functional relationships between different engine performance parameters during flight fast an adaptive network-based fuzzy inference system is employed. This increases significantly the robustness of the ERM system during highly transient in-flight conditions. Both the monitored and fault data uncertainties are considered in a multidimensional parameter space, with two probabilistic-based safety margins employed for fault detection and diagnostic, as follows: (i) Anomaly Detection Margin (ADM) and (ii) Fault Detection Margin (FDM).
Author: Rasul Mohammadi Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Stringent reliability and maintainability requirements for modern complex systems demand the development of systematic methods for fault detection and isolation. Many of such complex systems can be modeled as hybrid automata. In this thesis, a novel framework for fault diagnosis of hybrid automata is presented. Generally, in a hybrid system, two types of sensors may be available, namely: continuous sensors supplying continuous-time readings (i.e., real numbers) and threshold sensitive (discrete) sensors supplying discrete outputs (e.g., level high and pressure low). It is assumed that a bank of residual generators (detection filters) designed based on the continuous model of the plant is available. In the proposed framework, each residual generator is modeled by a Discrete-Event System (DES). Then, these DES models are integrated with the DES model of the hybrid system to build an Extended DES model. A "hybrid" diagnoser is then constructed based on the extended DES model. The "hybrid" diagnoser effectively combines the readings of discrete sensors and the information supplied by residual generators (which is based on continuous sensors) to determine the health status of the hybrid system. The problem of diagnosability of failure modes in hybrid automata is also studied here. A notion of failure diagnosability in hybrid automata is introduced and it is shown that for the diagnosability of a failure mode in a hybrid automaton, it is sufficient that the failure mode be diagnosable in the extended DES model developed for representing the hybrid automaton and residual generators. The diagnosability of failure modes in the case that some residual generators produce unreliable outputs in the form of false alarm or false silence signals is also investigated. Moreover, the problem of isolator (residual generator) selection is examined and approaches are developed for computing a minimal set of isolators to ensure the diagnosability of failure modes. The proposed hybrid diagnosis approach is employed for investigating faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner. A hybrid automaton model is obtained for the engine. A bank of residual generators is also designed, and an extended DES is constructed for the engine. Based on the extended DES model, a hybrid diagnoser is constructed and developed. The faults diagnosable by a purely DES diagnoser or by methods based on residual generators alone are also diagnosable by the hybrid diagnoser. Moreover, we have shown that there are faults (or groups of faults) in the fuel supply system and the nozzle actuator that can be isolated neither by a purely DES diagnoser nor by methods based on residual generators alone. However, these faults (or groups of faults) can be isolated if the hybrid diagnoser is used.
Author: Philip P. Walsh Publisher: John Wiley & Sons ISBN: 140515103X Category : Technology & Engineering Languages : en Pages : 664
Book Description
A significant addition to the literature on gas turbine technology, the second edition of Gas Turbine Performance is a lengthy text covering product advances and technological developments. Including extensive figures, charts, tables and formulae, this book will interest everyone concerned with gas turbine technology, whether they are designers, marketing staff or users.
Author: A M Y Razak Publisher: Elsevier ISBN: 184569340X Category : Technology & Engineering Languages : en Pages : 625
Book Description
Industrial Gas Turbines: Performance and Operability explains important aspects of gas turbine performance such as performance deterioration, service life and engine emissions. Traditionally, gas turbine performance has been taught from a design perspective with insufficient attention paid to the operational issues of a specific site. Operators are not always sufficiently familiar with engine performance issues to resolve operational problems and optimise performance.Industrial Gas Turbines: Performance and Operability discusses the key factors determining the performance of compressors, turbines, combustion and engine controls. An accompanying engine simulator CD illustrates gas turbine performance from the perspective of the operator, building on the concepts discussed in the text. The simulator is effectively a virtual engine and can be subjected to operating conditions that would be dangerous and damaging to an engine in real-life conditions. It also deals with issues of engine deterioration, emissions and turbine life. The combined use of text and simulators is designed to allow the reader to better understand and optimise gas turbine operation. Discusses the key factors in determining the perfomance of compressors, turbines, combustion and engine controls Explains important aspects of gas and turbine perfomance such as service life and engine emissions Accompanied by CD illustrating gas turbine performance, building on the concepts discussed in the text
Author: Rolf Isermann Publisher: Springer Science & Business Media ISBN: 3540303685 Category : Technology & Engineering Languages : en Pages : 478
Book Description
With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.
Author: Lakhmi C. Jain Publisher: CRC Press ISBN: 1000715124 Category : Computers Languages : en Pages : 363
Book Description
Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include: direct frequency converters electro-hydraulic systems motor control toaster control speech recognition vehicle routing fault diagnosis Asynchronous Transfer Mode (ATM) communications networks telephones for hard-of-hearing people control of gas turbine aero-engines telecommunications systems design Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.
Author: Ranjan Ganguli Publisher: CRC Press ISBN: 146650272X Category : Science Languages : en Pages : 255
Book Description
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.
Author: Dan M. Ghiocel
Author: Dan M. Ghiocel
Author: American Institute of Aeronautics and Astronautics
Author: Rasul Mohammadi
Author: 
Author: 
Author: Philip P. Walsh
Author: A M Y Razak
Author: Rolf Isermann
Author: Lakhmi C. Jain
Author: Ranjan Ganguli