Ron J. Patton – författare

Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.

There is an increasing demand for dynamic systems to become safer, more reliable and more economical in operation. This requirement extends beyond the normally accepted safety-critical systems e.g., nuclear reactors, aircraft and many chemical processes, to systems such as autonomous vehicles and some process control systems where the system availability is vital. The field of fault diagnosis for dynamic systems (including fault detection and isolation) has become an important topic of research. Many applications of qualitative and quantitative modelling, statistical processing and neural networks are now being planned and developed in complex engineering systems. Issues of Fault Diagnosis for Dynamic Systems has been prepared by experts in fault detection and isolation (FDI) and fault diagnosis with wide ranging experience.Subjects featured include: - Real plant application studies; - Non-linear observer methods; - Robust approaches to FDI; - The use of parity equations; - Statistical process monitoring; - Qualitative modelling for diagnosis; - Parameter estimation approaches to FDI; - Fault diagnosis for descriptor systems; - FDI in inertial navigation; - Stuctured approaches to FDI; - Change detection methods; - Bio-medical studies.Researchers and industrial experts will appreciate the combination of practical issues and mathematical theory with many examples. Control engineers will profit from the application studies.

Safety in industrial process and production plants is a concern of rising importance, especially if people would be endangered by a catastrophic system failure. On the other hand, because the control devices which are now exploited to improve the overall performance of industrial processes include both sophisticated digital system design techniques and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of failure. As a direct consequence of this, control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions as early as possible. One of the most promising methods for solving this problem is the "analytical redundancy" approach, in which residual signals are obtained. The basic idea consists of using an accurate model of the system to mimic the real process behaviour. If a fault occurs, the residual signal, i.e., the difference between real system and model behaviours, can be used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification.The problem is treated in all its aspects covering: choice of model structure; parameter identification; residual generation; fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques will be of interest to researchers in control and fault identification. Industrial control engineers interested in applying the latest methods in fault diagnosis will benefit from the practical examples and case studies.