Authors: Tomi Roinila; Mikko Huovinen; Matti Vilkko
Addresses: Department of Automation Science and Engineering, Tampere University of Technology, P.O. Box 692, FIN-33101, Tampere, Finland ' Yara Finland, P.O. Box 20, FI-71801, Siilinjärvi, Finland ' Department of Automation Science and Engineering, Tampere University of Technology, P.O. Box 692, FIN-33101, Tampere, Finland
Abstract: The systems in process industries are often large and complex. A typical system is characterised by hundreds or thousands of input and output variables and strong interactions between the sub processes. These factors often present difficulties for system monitoring. This paper presents the use of multiple-input multiple-output (MIMO) identification techniques in analysing the complex industrial systems online through frequency responses. Compared to more traditional identification methods, where a system is analysed through single-input experiments in the time domain, the proposed techniques provide several advantages such as shorter experiment time. This paper shows a practical approach and implementation to obtain the frequency responses. Experimental measurements are shown from a physical process which emulates the traditional headbox of a paper machine. The measurements are performed under normal operation conditions as well as under a fault condition. In addition, a simple computational method to recognise the fault condition based on the measured data is shown.
Keywords: process industries; system monitoring; excitation signal design; online fault detection; multiple-input multiple-output; MIMO identification; pseudo-random sequences; fault diagnosis.
International Journal of Industrial and Systems Engineering, 2014 Vol.17 No.4, pp.495 - 510
Available online: 28 Jul 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article