There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

This thesis describes the use of a class of sliding mode observers for fault detection and iso­ lation purposes. Existing work has shown that the equivalent output error injection term as­ sociated with the sliding mode observer, which represents the average value of the nonlinear switched term (which induces and maintains the sliding motion), if properly scaled, yields ac­ curate reconstructions of actuator faults. Existing observer design methods generate a certain class of observer gains, but do not utilise all degrees of freedom. In this thesis, a new method, exploiting this freedom is presented. The method uses Linear Matrix Inequalities and is easily implementable using standard software packages. New methods for accurately reconstructing sensor faults are also presented where appropriate filtering of certain measurable signals yields a fictitious system in which the original sensor faults are treated as actuator faults. Using the principles of actuator fault reconstruction in the existing work, sliding mode observers can be designed for the fictitious system to accurately reconstruct the sensor faults. This improves on the previous work where effectively only the steady state components of the sensor faults could be reconstructed. A new method using Linear Matrix Inequalities is presented, to syn­ thesise observers which can robustly reconstruct faults in the presence of a class system of uncertainty, minimising the effect of the uncertainty on the fault reconstruction in an £ 2 sense. The robust fault reconstruction scheme is demonstrated by means of a case study, which is a nonlinear model of an aero-engine. System identification is used to obtain a linear model of the engine. An uncertainty representation is also obtained about which the observer is designed. The results from the case study show that the robust fault reconstruction scheme works and is effective.

Sliding mode observers for fault detection and isolation

Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi-Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its online learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by "component level model" simulation of the General Electric XTE46 turbine engine.

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Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Intelligent Control and Health Management technology for aircraft propulsion systems is much more developed in the laboratory than in practice. With a renewed emphasis on reducing engine life cycle costs, improving fuel efficiency, increasing durability and life, etc., driven by various government programs, there is a strong push to move these technologies out of the laboratory and onto the engine. This paper describes the existing state of engine control and on-board health management, and surveys some specific technologies under development that will enable an aircraft propulsion system to operate in an intelligent way--defined as self-diagnostic, self-prognostic, self-optimizing, and mission adaptable. These technologies offer the potential for creating extremely safe, highly reliable systems. The technologies will help to enable a level of performance that far exceeds that of today s propulsion systems in terms of reduction of harmful emissions, maximization of fuel efficiency, and minimization of noise, while improving system affordability and safety. Technologies that are discussed include various aspects of propulsion control, diagnostics, prognostics, and their integration. The paper focuses on the improvements that can be achieved through innovative software and algorithms. It concentrates on those areas that do not require significant advances in sensors and actuators to make them achievable, while acknowledging the additional benefit that can be realized when those technologies become available. The paper also discusses issues associated with the introduction of some of the technologies.

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A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

In the paper, the results of a full-scale engine demonstration of a sensor failure detection algorithm are presented. The algorithm detects, isolates, and accommodates sensor failures using analytical redundancy. The experimental hardware, including the F100 engine, is described. Demonstration results were obtained over a large portion of a typical flight envelope for the F100 engine. They include both subsonic and supersonic conditions at both medium and full, nonafter burning, power. Estimated accuracy, minimum detectable levels of sensor failures, and failure accommodation performance for an F100 turbofan engine control system are discussed.

Turbofan engine demonstration of sensor failure detection

The objective of the advanced detection, isolation, and accommodation (ADIA) program is to improve the overall demonstrated reliability of digital electronic control systems for turbine engines. For this purpose, algorithms were developed which detect, isolate, and accommodate sensor failures using analytical redundancy. Preliminary results of a full scale engine demonstration of the ADIA algorithm are presented. Minimum detectable levels of sensor failures for an F100 turbofan engine control system are determined and compared to those obtained during a previous evaluation of this algorithm using a real-time hybrid computer simulation of the engine.

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Full-scale engine demonstration of an advanced sensor failure detection, isolation and accommodation algorithm: Preliminary results

A survey is presented of experimental and analytical experience of the NASA Lewis Research Center in engine response to inlet temperature and pressure distortions. This includes a description of the hardware and techniques employed, and a summary of the highlights of experimental investigations and analytical modeling. Distortion devices successfully simulated inlet distortion, and knowledge was gained about compression system response to different types of distortion. A list of NASA research references is included.

Summary of investigations of engine response to distorted inlet conditions

An uncertainty analysis was conducted to determine the bias and precision errors and total uncertainty of measured turbojet engine performance parameters. The engine tests were conducted as part of the Uniform Engine Test Program which was sponsored by the Advisory Group for Aerospace Research and Development (AGARD). With the same engines, support hardware, and instrumentation, performance parameters were measured twice, once during tests conducted in test cell number 3 and again during tests conducted in test cell number 4 of the NASA Lewis Propulsion Systems Laboratory. The analysis covers 15 engine parameters, including engine inlet airflow, engine net thrust, and engine specific fuel consumption measured at high rotor speed of 8875 rpm. Measurements were taken at three flight conditions defined by the following engine inlet pressure, engine inlet total temperature, and engine ram ratio: (1) 82.7 kPa, 288 K, 1.0, (2) 82.7 kPa, 288 K, 1.3, and (3) 20.7 kPa, 288 K, 1.3. In terms of bias, precision, and uncertainty magnitudes, there were no differences between most measurements made in test cells number 3 and 4. The magnitude of the errors increased for both test cells as engine pressure level decreased. Also, the level of the bias error was two to three times larger than that of the precision error.

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Mahmood Abdelwahab

Papers

Turbofan engine demonstration of sensor failure detection

Full-scale engine demonstration of an advanced sensor failure detection, isolation and accommodation algorithm: Preliminary results

Summary of investigations of engine response to distorted inlet conditions

Measurement Uncertainty for the Uniform Engine Testing Program Conducted at NASA Lewis Research Center

Full-scale engine demonstration of an advanced sensor failure detection isolation, and accommodation algorithm - Preliminary results