Tongwen Chen – författare

This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways:

·      from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and

·      from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian networks can be used to identify pair-wise relationships and network topology.

These methods rely on the notion of information fusion whereby process operating data is combined with qualitative process knowledge, to give a holistic picture of the system.

This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed.

The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. 

This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications.

This book fills a gap in existing literature by providing a comprehensive academic perspective on industrial alarm systems, offering systematic methodologies, practical techniques, and visual analytic tools for engineers to improve system performance and design. Modern industrial plants rely on computerized monitoring systems to track hundreds of process variables in real time, enabling operators to maintain safe and efficient conditions. Automatic industrial alarm systems play a crucial role in alerting operators to abnormalities, such as high vessel levels, that could lead to unsafe conditions if left unaddressed. While contemporary alarm systems can be plagued with issues like nuisance alarms, recent academic research has introduced advanced methodologies, like Markov chain theory and Bayesian estimation, to optimize alarm parameters and enhance system performance. By integrating these theoretical advancements into practical applications, the goal is to develop intelligent industrial alarm systems that leverage historical data and process knowledge to predict and prevent alarm floods, ultimately ensuring safer and more efficient plant operations.