Browsing by Author "Lindner, Brian Siegfried"

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    Exploiting process topology for optimal process monitoring
    (Stellenbosch : Stellenbosch University, 2014-12) Lindner, Brian Siegfried; Auret, Lidia; Stellenbosch University. Faculty of Engineering. Department of Process Engineering.
    ENGLISH ABSTRACT: Modern mineral processing plants are characterised by a large number of measured variables, interacting through numerous processing units, control loops and often recycle streams. Consequentially, faults in these plants propagate throughout the system, causing significant degradation in performance. Fault diagnosis therefore forms an essential part of performance monitoring in such processes. The use of feature extraction methods for fault diagnosis has been proven in literature to be useful in application to chemical or minerals processes. However, the ability of these methods to identify the causes of the faults is limited to identifying variables that display symptoms of the fault. Since faults propagate throughout the system, these results can be misleading and further fault identification has to be applied. Faults propagate through the system along material, energy or information flow paths, therefore process topology information can be used to aid fault identification. Topology information can be used to separate the process into multiple blocks to be analysed separately for fault diagnosis; the change in topology caused by fault conditions can be exploited to identify symptom variables; a topology map of the process can be used to trace faults back from their symptoms to possible root causes. The aim of this project, therefore, was to develop a process monitoring strategy that exploits process topology for fault detection and identification. Three methods for extracting topology from historical process data were compared: linear cross-correlation (LC), partial cross-correlation (PC) and transfer entropy (TE). The connectivity graphs obtained from these methods were used to divide process into multiple blocks. Two feature extraction methods were then applied for fault detection: principal components analysis (PCA), a linear method, was compared with kernel PCA (KPCA), a nonlinear method. In addition, three types of monitoring chart methods were compared: Shewhart charts; exponentially weighted moving average (EWMA) charts; and cumulative sum (CUSUM) monitoring charts. Two methods for identifying symptom variables for fault identification were then compared: using contributions of individual variables to the PCA SPE; and considering the change in connectivity. The topology graphs were then used to trace faults to their root causes. It was found that topology information was useful for fault identification in most of the fault scenarios considered. However, the performance was inconsistent, being dependent on the accuracy of the topology extraction. It was also concluded that blocking using topology information substantially improved fault detection and fault identification performance. A recommended fault diagnosis strategy was presented based on the results obtained from application of all the fault diagnosis methods considered.
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    Improving the performance of causality analysis techniques for automated fault diagnosis in mineral processing plants
    (Stellenbosch : Stellenbosch University, 2019-04) Lindner, Brian Siegfried; Auret, Lidia; Bauer, Margret; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: Modern mineral processing companies are driven towards improving productivity by leveraging existing processes optimally. This can be achieved by improving diagnosis of faults that degrade process performance to provide insightful and actionable information to process engineers. In mineral processing plants, units and variables are connected to each other through material ow, energy ow, and information ow. Faults propagate through a process along these interconnections, and can be traced back along their propagation paths to their root causes. Techniques have been developed for extracting these causal connections from historical process data. These techniques have proven successful for fault diagnosis in chemical processes. However, they have not been widely accepted by industry due to lack of automation of the techniques, complicated implementation, and complicated interpretation. This dissertation investigated the limitations of the causality analysis procedures currently available to process engineers as fault diagnosis tools and developed improvements on them. Improvements were developed and tested using a combination of simulated case studies and real world case studies of operational faults occurring in a mineral processing plant. Objective I: was to investigate the factors that a ect performance of causality analysis techniques. The use of transfer entropy for fault diagnosis in a minerals processing concentrator plant was demonstrated. The desired performance criteria of causality analysis techniques were then de ned in terms of: general applicability; automatability; interpretability; accuracy; precision; and computational complexity. The impact of process conditions on the performance of Granger causality and transfer entropy were then investigated. An analysis of variance (ANOVA) was performed to investigate the impact of process dynamics, fault dynamics, and the parameters on the accuracy of transfer entropy. Objective II: was to design a systematic work ow for application of causality analysis for fault diagnosis. The ANOVA was used to develop a novel relationship between the optimal transfer entropy parameters and the process and fault dynamics. This relationship was then placed within a systematic work ow developed for the application of transfer entropy for oscillation diagnosis, addressing the need for clear procedures and guidelines for data selection and parameter selection. The work ow was applied to an oscillation diagnosis case study from a minerals concentrator plant, and shown to provide a systematic approach to accurately determining the fault propagation path. Objective III: was to design a tool to aid the decision of which causality analysis method to select. A comparative analysis of Granger causality and transfer entropy for fault diagnosis based on the performance criteria de ned was performed. The comparison showed that transfer entropy was more precise, generalisable, and visually interpretable. Granger causality was more automatable, less computationally expensive, and easier to interpret. Guidelines were developed from these comparisons to aid users in deciding when to use Granger causality or transfer entropy Objective IV: was to present tools for interpretation of causal maps for root cause analysis. Methods for construction of causal maps from the results of the causality analysis calculation were presented, and methods for interpretation of causal maps. The usefulness of these techniques for diagnosis of real world case studies was demonstrated.