Abstract: In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of flow. The aim of this study was to find a data-driven method for identifying the direction of variation propagation using historical process data. Transfer entropy is a recently proposed method based on the probability density function (PDF) that measures directionality of variation with respect to time. An industrial case study illustrates the method which detects the influence of a temperature controller on downstream temperature measurements. A reversal of directionality was noted during a disturbance and a physical explanation offered
Statistical relationships among the variables of a complex system reveal a lot about its physical be...
Statistical relationships among the variables of a complex system reveal a lot about its physical be...
In this thesis, direction-dependent processes are described as processes whose responses differ in s...
In continuous chemical processes, variations of process variables usually travel along propagation p...
Abstract The discovery of cause-effect relationships in signals from industrial processes is a chal...
Oscillations in mineral processes can propagate through multiple units, causing important controlled...
The thesis is concerned with the development of data-driven methods for fault diagnosis of plant-wid...
Modern process industries are large and complex. Their units are highly interconnected with each oth...
Causality analysis techniques can be used for fault diagnosis in industrial processes. Multiple caus...
Transfer Entropy and Directed Information are information-theoretic measures of the directional depe...
We discuss a recently proposed quantity, called transfer entropy, which uses time series data to mea...
Transfer entropy (TE) is a model-free approach based on information theory to capture causality betw...
Industrial systems often encounter abnormal conditions due to various faults or external disturbance...
Information-theoretic quantities, such as entropy and mutual information (MI), can be used to quanti...
Production plants used in modern process industry must produce products that meet stringent environm...
Statistical relationships among the variables of a complex system reveal a lot about its physical be...
Statistical relationships among the variables of a complex system reveal a lot about its physical be...
In this thesis, direction-dependent processes are described as processes whose responses differ in s...
In continuous chemical processes, variations of process variables usually travel along propagation p...
Abstract The discovery of cause-effect relationships in signals from industrial processes is a chal...
Oscillations in mineral processes can propagate through multiple units, causing important controlled...
The thesis is concerned with the development of data-driven methods for fault diagnosis of plant-wid...
Modern process industries are large and complex. Their units are highly interconnected with each oth...
Causality analysis techniques can be used for fault diagnosis in industrial processes. Multiple caus...
Transfer Entropy and Directed Information are information-theoretic measures of the directional depe...
We discuss a recently proposed quantity, called transfer entropy, which uses time series data to mea...
Transfer entropy (TE) is a model-free approach based on information theory to capture causality betw...
Industrial systems often encounter abnormal conditions due to various faults or external disturbance...
Information-theoretic quantities, such as entropy and mutual information (MI), can be used to quanti...
Production plants used in modern process industry must produce products that meet stringent environm...
Statistical relationships among the variables of a complex system reveal a lot about its physical be...
Statistical relationships among the variables of a complex system reveal a lot about its physical be...
In this thesis, direction-dependent processes are described as processes whose responses differ in s...