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: Esmaeil Naderi Publisher: ISBN: Category : Languages : en Pages : 253
Book Description
This thesis contributes and provides solutions to the problem of fault diagnosis and estimation from three different perspectives which are i) fault diagnosis of nonlinear systems using nonlinear multiple model approach, ii) inversion-based fault estimation in linear systems, and iii) data-driven fault diagnosis and estimation in linear systems. The above contributions have been demonstrated to the gas turbines as one of the most important engineering systems in the power and aerospace industries. The proposed multiple model approach is essentially a hierarchy of nonlinear Kalman filters utilized as detection filters. A nonlinear mathematical model for a gas turbines is developed and verified. The fault vector is defined using the Gas Path Analysis approach. The nonlinear Kalman filters that correspond to the defined single or concurrent fault modes provide the conditional probabilities associated with each fault mode using the Bayes' law. The current fault mode is then determined based on the maximum probability criteria. The performance of both Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) are investigated and compared which demonstrates that the UKF outperforms the EKF for this particular application.The problem of fault estimation is increasingly receiving more attention due to its practical importance. Fault estimation is closely related to the problem of linear systems inversion. This thesis includes two contributions for the stable inversion of non-minimum phase systems. First, a novel methodology is proposed for direct estimation of unknown inputs by using only measurements of either minimum or non-minimum phase systems as well as systems with transmission zeros on the unit circle. A dynamic filter is then identified whose poles coincide with the transmission zeros of the system. A feedback is then introduced to stabilize the above filter dynamics as well as provide an unbiased estimation of the unknown input. The methodology is then applied to the problem of fault estimation and has been shown that the proposed inversion filter is unbiased for certain categories of faults. Second, a solution for unbiased reconstruction of general inputs is proposed. It is based on designing an unknown input observer (UIO) that provides unbiased estimation of the minimum phase states of the system. The reconstructed minimum phase states serve then as inputs for reconstruction of the non-minimum phase states. The reconstruction error for non-minimum phase states exponentially decrease as the estimation delay is increased. Therefore, an almost perfect reconstruction can be achieved by selecting the delay to be sufficiently large. The proposed inversion scheme is then applied to the output-tracking control problem. An important practical challenge is the fact that engineers rarely have a detailed and accurate mathematical model of complex engineering systems such as gas turbines. Consequently, one can find a trend towards data-driven approaches in many disciplines, including fault diagnosis. In this thesis, explicit state-space based fault detection, isolation and estimation filters are proposed that are directly identified from only the system input-output (I/O) measurements and through the system Markov parameters. The proposed procedures do not involve a reduction step and do not require identification of the system extended observability matrix or its left null space. Therefore, the performance of the proposed filters is directly connected to and linearly dependent on the errors in the Markov parameters estimation process. The estimation error dynamics is then derived in terms of the Markov parameters identification errors and directly synthesized from the healthy system I/O data. Consequently, the estimation errors have been effectively compensated for. The proposed data-driven scheme requires the persistently exciting condition for healthy input data which is not practical for certain real life applications and in particular to gas turbine engines. To address this issue, a robust methodology for Markov parameters estimation using frequency response data is developed. Finally, the performance of the proposed data-driven approach is comprehensively evaluated for the fault diagnosis and estimation problems in the gas turbine engines.
Author: Tamiru Alemu Lemma Publisher: Springer ISBN: 3319718711 Category : Technology & Engineering Languages : en Pages : 283
Book Description
This book provides a hybrid approach to fault detection and diagnostics. It presents a detailed analysis related to practical applications of the fault detection and diagnostics framework, and highlights recent findings on power plant nonlinear model identification and fault diagnostics. The effectiveness of the methods presented is tested using data acquired from actual cogeneration and cooling plants (CCPs). The models presented were developed by applying Neuro-Fuzzy (NF) methods. The book offers a valuable resource for researchers and practicing engineers alike.
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: Constantin Volosencu Publisher: BoD – Books on Demand ISBN: 1789844363 Category : Mathematics Languages : en Pages : 130
Book Description
This book offers a selection of papers in the field of fault detection and diagnosis, promoting new research results in the field, which come to join other publications in the literature. Authors from countries of four continents: United States of America, South Africa, China, India, Algeria and Croatia published worked examples and case studies resulting from their research in the field. Fault detection and diagnosis has a great importance in all industrial processes, to assure the monitoring, maintenance and repair of the complex processes, including all hardware, firmware and software. The book has four sections, determined by the application domain and the methods used: 1. Hybrid Computing Systems, 2. Power Systems, 3. Power Electronics and 4. Kalman Filtering. In the first section, the readers will find a technical report on fault diagnosis of hybrid computing systems, based on the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments. In the second section, two chapters are included: one of them presents a study on preventive maintenance and fault detection for wind turbine generators using statistical models and the second chapter presents a technical report on fault diagnosis for turbo-generators, based on the mechanical-electrical intersectional characteristics. The third section contains a technical report that presents some techniques of detection and localization of open-circuit faults in a three-phase voltage source inverter fed induction motor. The fourth section presents a theoretical study on the application of distributed discrete-time linear Kalman filtering with decentralized structure of sensors in fault residual generation.
Author: Moamar Sayed-Mouchaweh Publisher: Springer ISBN: 3319740148 Category : Technology & Engineering Languages : en Pages : 290
Book Description
Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. This book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains (inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc.). These approaches cover the different aspects of performing single/multiple online/offline parametric/discrete abrupt/tear and wear fault diagnosis in incremental/non-incremental manner, using different modeling tools (hybrid automata, hybrid Petri nets, hybrid bond graphs, extended Kalman filter etc.) for different classes of hybrid dynamic and complex systems.
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: Nabil Derbel Publisher: Springer Nature ISBN: 9811517460 Category : Technology & Engineering Languages : en Pages : 331
Book Description
This book focuses on unhealthy cyber-physical systems. Consisting of 14 chapters, it discusses recognizing the beginning of the fault, diagnosing the appearance of the fault, and stopping the system or switching to a special control mode known as fault-tolerant control. Each chapter includes the background, motivation, quantitative development (equations), and case studies/illustration/tutorial (simulations, experiences, curves, tables, etc.). Readers can easily tailor the techniques presented to accommodate their ad hoc applications.
Author: Sangchul Lee Publisher: Springer Nature ISBN: 9811926352 Category : Technology & Engineering Languages : en Pages : 1396
Book Description
This proceeding comprises peer-reviewed papers of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021), held from 15-17 November 2021 in Jeju, South Korea. This book deals with various themes on computational fluid dynamics, wind tunnel testing, flow visualization, UAV design, flight simulation, satellite attitude control, aeroelasticity and control, combustion analysis, fuel injection, cooling systems, spacecraft propulsion and so forth. So, this book can be very helpful not only for the researchers of universities and academic institutes, but also for the industry engineers who are interested in the current and future advanced topics in aerospace technology.
Author: Rasul Mohammadi
Author: Esmaeil Naderi
Author: Tamiru Alemu Lemma
Author: Ranjan Ganguli
Author: Ji Zhou
Author: Constantin Volosencu
Author: Moamar Sayed-Mouchaweh
Author: Rolf Isermann
Author: Nabil Derbel
Author: Sangchul Lee