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

Forecasting with artificial neural networks: the state of the art

Economic Control of Quality of Manufactured Product.

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.

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Neural networks for classification: a survey

Neural networks for control systems: a survey

The concepts involved in the formulation of artificial neural networks are introduced. Their suitability for solving some process engineering problems is discussed and illustrated using results obtained from both simulation studies and applications to industrial process data. In the latter, neural network models were used to provide estimates of biomass concentration in industrial fermentation systems and of top product composition of an industrial distillation tower. Measurements from established instruments such as off-gas carbon dioxide in the fermenter and overheads temperature in the distillation column were used as the secondary variables for the respective processes. The advantage of using these estimates for feedback control is demonstrated. The possibility of using neural network models directly in model based control strategies is also considered. The range of applications is an indication of the utility of artificial neural network methodologies within a process engineering environment.

M.T. Tham

Papers

Artificial neural networks in process engineering

Artificial neural networks in process estimation and control

Soft-sensors for process estimation and inferential control

A procedure for determining the topology of multilayer feedforward neural networks

Towards improved penicillin fermentation via artificial neural networks