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://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

Computational Intelligence: An Introduction

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

Brief paper: On the use of adaptive updating rules for actuator and sensor fault diagnosis

https://nrec.ir/sites/default/files/articles/Optimal-tuning%20PID%20control%20for%20industrial%20systems.pdf

Optimal-tuning PID control for industrial systems

Sensor fault-tolerant control of a rail traction drive

https://www.cs.ox.ac.uk/people/vasile.palade/papers/Palade_CongresIFAC2002.pdf

Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods

Methods for tuning PID controllers so that the closed loop response closely matches that of a reference model are described. These approaches are based on direct search optimisation using an identified model and can be applied to a wide variety of industrial processes, including those with high order dynamics. Results from the application to controller tuning for two distinct industrial systems are presented which demonstrate both the power and the limitations of the technique.

http://ieeexplore.ieee.org/iel5/2218/16635/00768660.pdf

S. Daley

Papers

Brief paper: On the use of adaptive updating rules for actuator and sensor fault diagnosis

Optimal-tuning PID control for industrial systems

Sensor fault-tolerant control of a rail traction drive

Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods

Optimal PID tuning using direct search algorithms