3 results for Abd Majid, NA

  • Multivariate statistical monitoring of the aluminium smelting process

    Abd Majid, NA; Taylor, MP; Chen, JJJ; Young, BR (2011)

    Journal article
    The University of Auckland Library

    This paper describes the development of a new ‘cascade’ monitoring system for the aluminiumsmeltingprocess that uses latent variable models. This system is based on the changes of variability patterns within a feeding cycle which are used to provide indications of faults and their possible causes. The system has been tested offline using 31 data sets. The performance of the system to detect an anode effect has been compared with a typical latent variable model that monitors the change of behaviour at every time instant. The results show that the ‘cascade’ monitoring system is able to detect abnormal events. It was possible to relate each event with specific patterns associated with abnormalities thus facilitating later fault diagnosis.

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  • Detecting abnormalities in aluminium reduction cells based on process events using multi-way principal component analysis (MPCA)

    Abd Majid, NA; Young, Brent; Taylor, Mark; Chen, JJJ (2009)

    Conference item
    The University of Auckland Library

    In the aluminium industry optimal production and quality products are major process targets. One way to achieve these targets is by improving the process control of aluminium reduction cells, and this is the aim of this research. This research proposes to apply an advanced multivariate control chart to aluminium reduction cells in a manner which provides new insights into process abnormalities and their diagnosis. The proposed approach uses multi-way principal component analysis to observe the movement of data towards abnormality after process events. Preliminary results showed that using the proposed approach could detect anode spikes after anode changing or tapping. Data with anode spikes present moved in a different direction than the data with anode spikes absent. An anode spike trajectory could be set up based on this discrimination. Data which move towards the anode spike trajectory have a high possibility of having anode spikes. Therefore based on this trajectory, the cell could be monitored ahead of time for spikes, and operations may take action to search for them much earlier. This will lead to a real-time fault detection system and is expected to assist process engineers in improving the process control of aluminium reduction cells.

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  • Aluminium Process Fault Detection and Diagnosis

    Abd Majid, NA; Taylor, Mark; Chen, John; Young, Brent (2015)

    Journal article
    The University of Auckland Library

    The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators??? view of an industrial plant, so that it permits a situation-oriented action in real working environments.

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