Diagnosing faults in aluminium processing by using multivariate statistical approaches

Multivariate statistical approaches are expected to detect and diagnose faults effectively for complex materials processing including steel, iron, copper and aluminium processing. This advanced monitoring of materials processing is essential to address abnormal or faulty conditions in a timely manner. In the aluminium-smelting process, late diagnosis of abnormal conditions such as an anode effect can result in an increase of energy consumption and emission of greenhouse gases. In this article, a new statistical framework is proposed that is based on hierarchal diagnostic approach to diagnose two groups of faults, anode faults and non-anode faults. The system diagnoses faults by predicting the type of fault based on continuous, non-linear and multivariate process data using discriminant partial least squares (DPLS). The new system goes beyond the typical multivariate system in that it also includes the dynamic behaviour of the process during anode changing and alumina feeding. The results of performance evaluation of the new diagnosis system tested using real-data show that the system can diagnose the two groups of faults.

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

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

[3]  Q. Peter He,et al.  A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .

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

[5]  Theodora Kourti,et al.  Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications , 2003, Annu. Rev. Control..

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

[7]  Dimitrios I. Gerogiorgis,et al.  Light Metals 2003 , 2003 .

[8]  Steve McKnight.  Chemical and Materials Information Management for Sustainable Engineering and Design , 2010 .

[9]  Ahmet Palazoglu,et al.  Introduction to Process Control , 2005 .

[10]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[11]  Venkat Venkatasubramanian,et al.  Challenges in the industrial applications of fault diagnostic systems , 2000 .

[12]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

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

[14]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .

[15]  Geoffrey P. Bearne.  The development of aluminum reduction cell process control , 1999 .

[16]  Daniel Aguado,et al.  Multivariate statistical monitoring of continuous wastewater treatment plants , 2008, Eng. Appl. Artif. Intell..

[17]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

[18]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

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

[20]  Kwang-Jae Kim,et al.  Fault diagnosis of batch processes using discriminant model , 2004 .

[21]  P. Jose,et al.  The dopaminergic system in hypertension. , 2007, Clinical science.