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

https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionPart1.pdf

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

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

This paper presents a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear uncertain dynamic systems. A detection and approximation estimator is used for online health monitoring. Once a fault is detected, a bank of isolation estimators is activated for the purpose of fault isolation. A key design issue of the proposed fault isolation scheme is the adaptive residual threshold associated with each isolation estimator. A fault that has occurred can be isolated if the residual associated with the matched isolation estimator remains below its corresponding adaptive threshold, whereas at least one of the components of the residuals associated with all the other estimators exceeds its threshold at some finite time. Based on the class of nonlinear uncertain systems under consideration, an isolation decision scheme is devised and fault isolability conditions are given, characterizing the class of nonlinear faults that are isolable by the robust fault isolation scheme. The nonconservativeness of the fault isolability conditions is illustrated by deriving a subclass of nonlinear systems and of faults for which these conditions are also necessary for fault isolability. Moreover, the analysis of the proposed fault isolation scheme provides rigorous analytical results concerning the fault isolation time. Two simulation examples are given to show the effectiveness of the fault diagnosis methodology.

/pdf/a-robust-detection-and-isolation-scheme-for-abrupt-and-2czu0guwgr.pdf

A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems

Brief paper: Nonlinear robust fault reconstruction and estimation using a sliding mode observer

A bilinear fault detection observer

A bilinear fault detection observer is proposed for bilinear systems with unknown input. A sufficient condition for the existence of the observer is given. The residual generated by this observer is decoupled from the unknown input. The method is applied to a hydraulic test rig to detect and isolate a large group of simulated faults. The effectiveness of the method in fault detection and isolation for the hydraulic system is demonstrated by real data simulation.

A bilinear fault detection observer and its application to a hydraulic drive system

Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines

This paper applies a fault detection strategy to a Jaguar car engine. Real data, over several engine speeds, is used to obtain an improved model of a subsystem comprising of the air intake, the manifold dynamics and the engine pumping. Modelling of an air leak is also considered. Three practical fault scenarios are assessed, deemed important by the manufacturer. A nonlinear observer method is used to detect practical sensor faults and an air leak in the manifold. Isolation logic is designed with respect to a restricted set of sensor measurements. The effectiveness of both modelling and fault detection axe assessed. A practical aim is to limit engine pollutants caused by system faults.

Improved SI engine modelling techniques with application to fault detection

A fault detection method is proposed for generating robust residuals for a bilinear system with unknown inputs. Necessary and sufficient conditions for the existence of a fault detection observer are given. The method is applied to a hydraulic system and simulation results are given.<
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D.N. Shields

Papers

A bilinear fault detection observer

A bilinear fault detection observer and its application to a hydraulic drive system

Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines

Improved SI engine modelling techniques with application to fault detection

A nonlinear fault detection method for a hydraulic system