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

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

Renewable energy sources like wind energy are copiously available without any limitation. Wind turbines are used to tap the potential of wind energy, which is available in millions of MW. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. We reviewed different techniques, methodologies and algorithms developed to monitor the performance of wind turbine as well as for an early fault detection to keep away the wind turbines from catastrophic conditions due to sudden breakdowns. To keep the wind turbine in operation, implementation of condition monitoring system (CMS) and fault detection system (FDS) is paramount and for this purpose ample knowledge of these two types of systems is mandatory. So, an attempt has been made in this direction to review maximum approaches related to CMS and FDS in this piece of writing.

Condition monitoring and fault detection of wind turbines and related algorithms: A review

This paper is the second of a two-part review of methods for automated fault detection and diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R research and development community to the body of FDD and prognostics developments in other fields as well as advancements in the field of HVAC&R. The first part of the review focused on generic FDD and prognostics, provided a framework for categorizing methods, described them, and identified their primary strengths and weaknesses (Katipamula and Brambley 2005). In this paper we address research and applications specific to the fields of HVAC&R, provide a brief discussion on the current state of diagnostics in buildings, and discuss the future of automated diagnostics in buildings.

/pdf/methods-for-fault-detection-diagnostics-and-prognostics-for-3us50wg51h.pdf

Methods for Fault Detection, Diagnostics and Prognostics for Building Systems - A Review Part II

Current development in the theory of FDI

Current Developments in the Theory of FDI

New developments using AI in fault diagnosis

Fuzzy logic and neural network applications to fault diagnosis

In this paper a neural network based fault detection and isolation concept for supervision of technical processes is introduced. Three different possibilities to employ neural networks for fault diagnosis are discussed. In most existing schemes the steps of residual generation and residual evaluation are performed on the basis of analytical process knowledge. Here neural networks are proposed for these tasks. First of all a neural network can be used instead of a mathematical model for residual generation, secondly another neural network can be trained to perform the classification task for residual evaluation and therefore fault isolation. A third possibility is a one-step diagnosis (OSD), where one neural network is trained to directly detect and isolate possible faults from the available measurements without the need for prior generation of intermediate signals as residuals. Results from the application of a restricted Coulomb energy neural network (RCE) to the residual evaluation and alternatively to the one-step diagnosis at an industrial actuator benchmark problem are presented.

Birgit Köppen-Seliger

Papers

Current development in the theory of FDI

Current Developments in the Theory of FDI

New developments using AI in fault diagnosis

Fuzzy logic and neural network applications to fault diagnosis

Fault detection and isolation in technical processes with neural networks