Browsing by Author "Van Zijl, Natali"

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    Improving the interpretability of causality maps for fault identification
    (Stellenbosch : Stellenbosch University, 2020-12) Van Zijl, Natali; Louw, Tobias M.; Bradshaw, S. M.; Auret, Lidia; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: Worldwide competition forces modern mineral processing plants to operate at high productivity. This high productivity is achieved by implementing process monitoring to maintain the desired operating conditions. However, a fault originating in one section of a plant can propagate throughout the plant and so obscure its root cause. Causality analysis is a method that identifies the cause-effect relationships between process variables and presents these in a causality map which can be used to track the propagation path of a fault back to its root cause. A major obstacle to the wide acceptance of causality analysis as a tool for fault diagnosis in industry is the poor interpretability of causality maps. This study identified, proposed and assessed ways to improve the interpretability of causality maps for fault identification. All approaches were tested on a simulated case study and the resulting maps compared to a standard causality map or its transitive reduction. The ideal causality map was defined and all comparisons were performed based on its characteristics. Causality maps were produced using conditional Granger causality (GC), with a novel heuristic approach for selecting sampling period and time window. Conditional GC was found to be ill-suited to plant-wide causality analysis, due to large data requirements, poor model order selection using AIC, and inaccuracy in the presence of multiple different residence times and time delays. Methods to incorporate process knowledge to constrain connections and potential root causes were investigated and found to remove all spurious connections and decrease the pool of potential root cause variables respectively. Tools such as visually displaying node rankings on the causality map and incorporating sliders to manipulate connections and variables were also investigated. Furthermore, a novel hierarchical approach for plant-wide causality analysis was proposed, where causality maps were constructed in two subsequent stages. In the first stage, a less-detailed plant-wide map was constructed using representatives for groups of variables, and used to localise the fault to one of those groups of variables. Variables were grouped according to plant sections or modules identified in the data, and the first principal component (PC1) was used to represent each group (PS-PC1 and Mod-PC1 respectively). PS-PC1 was found to be the most promising approach, as its plant-wide map clearly identified the true root cause location, and the stage-wise application of conditional GC significantly reduced the required number of samples from 13 562 to 602. Lastly, a usability study in the form of a survey was performed to investigate the potential for industrial application of the tools and approaches presented in this study. Twenty responses were obtained, with participants consisting of Stellenbosch University final-year/postgraduate students, employees of an industrial IoT firm, and Anglo American Platinum employees. Main findings include that process knowledge is vital; grouping variables improves interpretability by decreasing the number of nodes; accuracy must be maintained during causality map simplification; and sliders add confusion by causing significant changes in the causality map. In addition, survey results found PS-PC1 to be the most user-friendly approach, further emphasizing its potential for application in industry.