Aluminium Process Fault Detection and Diagnosis

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.

[1]  Jayson Tessier,et al.  Increasing Potlife of Hall–Héroult Reduction Cells Through Multivariate On-Line Monitoring of Preheating, Start-Up, and Early Operation , 2010 .

[2]  Jayson Tessier,et al.  Estimation of alumina content of anode cover materials using multivariate image analysis techniques , 2008 .

[3]  Andrew Y. C. Nee,et al.  Augmented reality applications in design and manufacturing , 2012 .

[4]  Larry E. Banta,et al.  Noise Classification in the Aluminum Reduction Process , 2016 .

[5]  Tsuyoshi Yamamoto,et al.  3D puzzle guidance in augmented reality environment using a 3D desk surface projection , 2011, 3DUI.

[6]  Brent R. Young,et al.  Diagnosing faults in aluminium processing by using multivariate statistical approaches , 2012, Journal of Materials Science.

[7]  Salvador García Muñoz,et al.  Data-based latent variable methods for process analysis, monitoring and control , 2005, Comput. Chem. Eng..

[8]  Christine W. Chan,et al.  Artificial intelligence for monitoring and supervisory control of process systems , 2007, Eng. Appl. Artif. Intell..

[9]  Brandon Stutzman,et al.  MARTI: Mobile Augmented Reality Tool for Industry , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[10]  Nazatul Aini Abd Majid,et al.  Aluminium process fault detection by Multiway Principal Component Analysis , 2011 .

[11]  Manabu Kano,et al.  Recent developments and industrial applications of data-based process monitoring and process control , 2006 .

[12]  M. P. Taylor,et al.  Advances in Process Control for Aluminium Smelters , 2007 .

[13]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[14]  Xu Liu,et al.  Design and fabrication of an off-axis see-through head-mounted display with an x-y polynomial surface. , 2010, Applied optics.

[15]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[16]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[17]  Gudrun Klinker,et al.  The Intelligent Welding Gun: Augmented Reality for Experimental Vehicle Construction , 2004 .

[18]  Jules Thibault,et al.  Neural networks for the identification of the aluminium electrolysis process , 1998 .

[19]  João Viana da Fonseca Neto,et al.  Pattern Identification for Feed Control Strategy Using Fuzzy Neural Algorithm , 2009, 2009 11th International Conference on Computer Modelling and Simulation.

[20]  Michael S. Dudzic,et al.  An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .

[21]  William Johns,et al.  Computer‐Aided Chemical Engineering , 2011 .

[22]  Blandine de Laval,et al.  STARMATE : Using Augmented Reality technology for computer guided maintenance of complex mechanical elements , 2002 .

[23]  Woodrow Barfield,et al.  Boeing’s Wire Bundle Assembly Project , 2001 .

[24]  Erik Johansson,et al.  Multivariate process and quality monitoring applied to an electrolysis process: Part I. Process supervision with multivariate control charts , 1998 .

[25]  Morten Hovd,et al.  Detection of abnormal alumina feed rate in aluminium electrolysis cells using state and parameter estimation , 2006 .

[26]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[27]  Nassir Navab.  Developing killer apps for industrial augmented reality , 2004, IEEE Computer Graphics and Applications.

[28]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[29]  Panu Harmo,et al.  ETÄLÄ - Virtual Reality Assisted Telepresence System for Remote Control and Maintenance , 2000 .

[30]  Steven K. Feiner,et al.  Knowledge-based augmented reality , 1993, CACM.

[31]  Fumio Kishino,et al.  Augmented reality: a class of displays on the reality-virtuality continuum , 1995, Other Conferences.

[32]  Theodora Kourti,et al.  Application of latent variable methods to process control and multivariate statistical process control in industry , 2005 .

[33]  Jean-Yves Didier,et al.  AMRA: Augmented Reality Assistance for Train Maintenance Tasks , 2005 .

[34]  Mark Billinghurst,et al.  Augmented assembly using a mobile phone , 2008, ISMAR.

[35]  Michael J. Hautus,et al.  Operational Decision Making in Aluminium Smelters , 2009, HCI.

[36]  Holger Regenbrecht,et al.  Augmented reality projects in the automotive and aerospace industries , 2005, IEEE Computer Graphics and Applications.

[37]  Yongtian Wang,et al.  Design of an optical see-through head-mounted display with a low f-number and large field of view using a freeform prism. , 2009, Applied optics.

[38]  Paula Savioja,et al.  Developing a Mobile, Service-Based Augmented Reality Tool for Modern Maintenance Work , 2007, HCI.

[39]  Steven K. Feiner,et al.  Opportunistic Tangible User Interfaces for Augmented Reality , 2010, IEEE Transactions on Visualization and Computer Graphics.

[40]  M. Vajta,et al.  Adaptive Prediction of Anode-effects in Aluminium Reduction Cells , 1987 .

[41]  C. McGreavy,et al.  Data Mining and Knowledge Discovery for Process Monitoring and Control , 1999 .

[42]  Xue Z. Wang,et al.  Data Mining and Knowledge Discovery — an Overview , 1999 .