Pawel D. Domanski – författare

Due to the popularity of artificial intelligence today, scientists and practitioners tend to ignore the achievements of statistical methods. This book aims to re-establish statistical methods, especially for control practitioners, for whom the task of assessing control performance is important.

The author introduces the elements of statistical theory, including basic statistical concepts and the description of distributions. Extending the most common observations toward the tails and extreme statistics, he demonstrates the robust statistics approach that deals with the tails, followed by the description of methods that can visualize and bring forward statistical properties as long as the derivative issues. By addressing statistical issues of sustainability, extreme statistics, non-Gaussianity, L-moments, tail index and index ratio diagrams, the author aims to change traditional concepts and broaden the scope of available methods. Simulation case studies and real industrial cases are also presented.

The book will be of interest to site control engineers and scholars assessing control systems and process performance.

Due to the popularity of artificial intelligence today, scientists and practitioners tend to ignore the achievements of statistical methods. This book aims to re-establish statistical methods, especially for control practitioners, for whom the task of assessing control performance is important.

The author introduces the elements of statistical theory, including basic statistical concepts and the description of distributions. Extending the most common observations toward the tails and extreme statistics, he demonstrates the robust statistics approach that deals with the tails, followed by the description of methods that can visualize and bring forward statistical properties as long as the derivative issues. By addressing statistical issues of sustainability, extreme statistics, non-Gaussianity, L-moments, tail index and index ratio diagrams, the author aims to change traditional concepts and broaden the scope of available methods. Simulation case studies and real industrial cases are also presented.

The book will be of interest to site control engineers and scholars assessing control systems and process performance.

This book presents a comprehensive review of currently available Control Performance Assessment methods. It covers a broad range of classical and modern methods, with a main focus on assessment practice, and is intended to help practitioners learn and properly perform control assessment in the industrial reality. Further, it offers an educational guide for control engineers, who are currently in high demand in the industry. 

The book consists of three main parts. Firstly, a comprehensive review of available approaches is presented and discussed. The classical canon methods are extended with a discussion of nonlinear and complex alternative measures using non-Gaussian statistics, persistence and fractional calculations. Secondly, the methods’ applicability aspects are visualized with the aid of computer simulations, covering the most popular control philosophies used in the process industry. Lastly, a critical review of the methods discussed, on the basis of real-world industrial examples, rounds out the coverage. 

Outliers play an important, though underestimated, role in control engineering. Traditionally they are unseen and neglected. In opposition, industrial practice gives frequent examples of their existence and their mostly negative impacts on the control quality. The origin of outliers is never fully known. Some of them are generated externally to the process (exogenous), like for instance erroneous observations, data corrupted by control systems or the effect of human intervention. Such outliers appear occasionally with some unknow probability shifting real value often to some strange and nonsense value. They are frequently called deviants, anomalies or contaminants. In most cases we are interested in their detection and removal.

However, there exists the second kind of outliers. Quite often strange looking data observations are not artificial data occurrences. They may be just representatives of the underlying generation mechanism being inseparable internal part of the process (endogenous outliers). In such a case they are not wrong and should be treated with cautiousness, as they may include important information about the dynamic nature of the process. As such they cannot be neglected nor simply removed. The Outlier should be detected, labelled and suitably treated. These activities cannot be performed without proper analytical tools and modeling approaches. There are dozens of methods proposed by scientists, starting from Gaussian-based statistical scoring up to data mining artificial intelligence tools. The research presented in this book presents novel approach incorporating non-Gaussian statistical tools and fractional calculus approach revealing new data analytics applied to this important and challenging task.

The proposed book includes a collection of contributions addressing different yet cohesive subjects, like dynamic modelling, classical control, advanced control, fractional calculus, statistical analytics focused on an ultimate goal: robust and outlier-proof analysis. All studied problems show that outliers play an important role and classical methods, in which outlier are not taken into account, do not give good results. Applications from different engineering areas are considered such as semiconductor process control and monitoring, MIMO peltier temperature control and health monitoring, networked control systems, and etc.