Browsing by Author "Landman, Rinat"
Now showing 1 - 4 of 4
- Results Per Page
- Sort Options
- Data-based causality analysis by exploiting process connectivity information
School of Chemical Technology | Doctoral dissertation (article-based)(2019) Landman, RinatIndustrial systems are often subjected to abnormal conditions due to faulty operations or external disturbances. Faults can easily propagate via the process components through material or information flows, thereby deteriorate the process performance and product quality and increase the operational costs. Therefore, it is of major importance to detect a fault, locate its root cause and reveal how it had propagated within the system. Capturing the causality of a system has a key role in fault diagnosis due to its ability to identify the root cause of a fault and retrace its propagation path. Thus far, several data-based methods have been proposed in order to capture causality from time series corresponding to process variables. However, the majority of the data-based methods suffer from several limitations and deficiencies which compromise their ability to provide adequate results, particularly when investigating complex systems with a high level of connectivity. This thesis proposes a hybrid causal analysis which automatically incorporates the information on the process connectivity into data-based analysis using a specialized search algorithm. The analysis aims to enhance the results accuracy, minimize the computational effort and to successfully tackle multivariate complex systems. This thesis entails four methodologies for a hybrid causal analysis based on the following causality estimators: Granger causality, transfer entropy, nearest neighbors and non-parametric multiplicative regression causality estimator. The hybrid causal analysis is successfully demonstrated on an industrial board machine using each of the causality estimators. The analysis aims to detect the propagation of an oscillatory disturbance due to valve stiction within the control loops of the drying section of the machine. Finally, the results of each causality estimator are evaluated and the methods are compared. The obtained results show that the hybrid causal analysis produced an enhanced causal model which depicts the oscillation propagation path and its root cause. Taken together, the findings of this study suggest that the connectivity information is essential for obtaining an adequate causal model when investigating complex systems. A natural progression of this work could be to implement the proposed hybrid analysis using other case studies with different types of faults. - Data-driven causal analysis and its application on a large-scale board machine
School of Chemical Engineering | Master's thesis(2013) Landman, RinatIn large-scale chemical processes, disturbances can easily propagate through the process units and thereby adversely affect the overall process performance. In recent years, causal analysis has played a key role in the diagnosis of plant wide disturbances. Causal analysis can disclose the root cause and reveal the path in which the disturbance propagated. Data-driven causal analysis utilizes historical process data in the form of time series and examines to what extent the time series influence each other. If directionality between time series is inferred, it is taken as an evidence for a cause-and-effect relationship. Data driven causal analysis can efficiently complement knowledge-based causal analysis and provide valuable insights on process dynamics with minimal efforts. The aim of this thesis is to apply several data-based causal analyses on an industrial case study of a paper board machine and to evaluate the effectiveness of each method. The theoretical part of this thesis provides an overview of the main data-based methods for identifying causal relationships between time series. The experimental part contains a detailed description of the process case-study. The analysis focused on the drying section of the board machine due to its importance in the board making process and the high share of faults associated with this section. The time domain and frequency domain methods for detecting causal influences were applied to the investigated case study. The outcome of each method was a causal model in the form of a directed graph describing the influences among the variables in the process. All the methods applied were able to identify the most powerful interactions between the variables. However, all the methods produced somewhat spurious results; thus, process knowledge was found to be essential in the modelling procedure. In addition, root cause analysis based on the cross-correlation and the frequency domain methods was successfully applied and the root cause of the disturbance was identified. In the future, other non-linear data-based methods could be employed in order to supplement the linear methods applied in this study. - Fault Propagation Analysis by Implementing Nearest Neighbors Method Using Process Connectivity
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019-09) Landman, Rinat; Jämsä-Jounela, Sirkka-LiisaIndustrial systems often encounter abnormal conditions due to various faults or external disturbances which deteriorate the process performance. In such cases, it is essential to detect and eliminate the root cause of the faulty condition as early as possible in order to minimize its adverse effect on the entire process performance. Capturing the process causality plays a key role in identifying the propagation path of faults and their root cause. In recent times, several data-based methods have been developed in order to capture causality from the measured process data. However, each of the methods suffers from several limitations and deficiencies which might compromise their ability to provide an adequate causal model, especially in multivariate (MV) systems. This paper proposes a new methodology for retracing the propagation path of oscillation using a nearest neighbors method by utilizing the information on process connectivity. The two-phase methodology yields a directionality measure based on the type of connectivity in the process using a unique search algorithm. In phase I, the bivariate directionality measure is calculated to include only the interactions that are considered as direct based on the plant topology. In phase II, a new MV directionality measure based on the nearest neighbors method is introduced in order to exclude indirect interactions. The methodology is successfully demonstrated on industrial board machine exhibiting oscillations in its drying section. - Hybrid causal analysis combining a nonparametric multiplicative regression causality estimator
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019) Landman, Rinat; Jämsä-Jounela, Sirkka-LiisaIndustrial processes are often subjected to abnormal events such as faults or external disturbances which can easily propagate via the process units. Establishing causal dependencies among process measurements has a key role in fault diagnosis due to its ability to identify the root cause of a fault and its propagation path. This paper proposes a hybrid nonlinear causal analysis based on nonparametric multiplicative regression (NPMR) for identifying the propagation of an oscillatory disturbance via control loops. The NPMR causality estimator addresses most of the limitations of the linear model-based methods and it can be applied to both bivariate and multivariate estimations without any modifications to the method parameters. Moreover, the NPMR-based estimations can be used to pinpoint the root cause of a fault. The process connectivity information is automatically integrated into the causal analysis using a specialized search algorithm. Thereby, it enables to efficiently tackle industrial systems with a high level of connectivity and enhance the quality of the results. The proposed approach is successfully demonstrated on an industrial board machine exhibiting oscillations in its drying section due to valve stiction and. The NPMR-based estimator produced highly accurate results with relatively low computational effort compared with the linear Granger causality and other nonlinear causality estimators.