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

Supervision, fault-detection and fault-diagnosis methods — An introduction

This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

https://romisatriawahono.net/lecture/rm/survey/softcomputing/Kar%20-%20Neuro%20Fuzzy%20Systems%20-%202014.pdf

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

The general fault-tolerant control method described in the article addresses actuator and sensor faults, which often affect highly automated systems. These faults correspond to a loss of actuator effectiveness or fault sensor measurements. After describing these faults, a fault estimation and compensation method was proposed. In addition to providing information to operators concerning the system operating conditions, the fault diagnosis module is especially important in fault-tolerant control systems where one needs to know exactly which element is faulty to react safely. The method's abilities to compensate for such faults are illustrated by applying it to a winding machine, which represents a subsystem of many industrial systems. The results show that once the fault is detected and isolated, it is easy to reduce its effect on the system, and process control is resumed with degraded performances close to nominal ones. Thus, stopping the system immediately can be avoided. However, the limits of this method are reached when there is the complete loss of an actuator. In this case, only a hardware redundancy is effective and could ensure performance reliability. The method proposed here assumes the availability of the state variables for measurement.

Fault-tolerant control in dynamic systems: application to a winding machine

The degree of vagueness of variables, process description, and automation functions is considered and is shown. Where quantitative and qualitative knowledge is available for design and information processing within automation systems. Fuzzy-rule-based systems with several levels of rules form the basis for different automation functions. Fuzzy control can be used in many ways, for normal and for special operating conditions. Experience with the design of fuzzy controllers in the basic level is summarized, as well as criteria for efficient applications. Different fuzzy control schemes are considered, including cascade, feedforward, variable structure, self-tuning, adaptive and quality control leading to hybrid classical/fuzzy control systems. It is then shown how fuzzy logic approaches can be applied to process supervision and to fault diagnosis with approximate reasoning on observed symptoms. Based on the properties of fuzzy logic approaches the contribution gives a review and classification of the potentials of fuzzy logic in process automation.

Mihiar Ayoubi

Papers

Methods of fault diagnosis

Neuro-fuzzy systems for diagnosis

Fault Diagnosis with Dynamic Neural Structure and Application to a Turbocharger

Fuzzy systems design based on a hybrid neural structure and application to the fault diagnosis of technical processes

Real-time supervision for diesel engine injection