Diagnostic monitoring of dynamic systems using artificial immune systems
| dc.contributor.advisor | Aldrich, C. | |
| dc.contributor.author | Maree, Charl | en_ZA |
| dc.contributor.other | University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering. | |
| dc.date.accessioned | 2008-02-06T09:35:38Z | en_ZA |
| dc.date.accessioned | 2010-06-01T08:33:04Z | |
| dc.date.available | 2008-02-06T09:35:38Z | en_ZA |
| dc.date.available | 2010-06-01T08:33:04Z | |
| dc.date.issued | 2006-12 | en_ZA |
| dc.description | Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006. | |
| dc.description.abstract | The natural immune system is an exceptional pattern recognition system based on memory and learning that is capable of detecting both known and unknown pathogens. Artificial immune systems (AIS) employ some of the functionalities of the natural immune system in detecting change in dynamic process systems. The emerging field of artificial immune systems has enormous potential in the application of fault detection systems in process engineering. This thesis aims to firstly familiarise the reader with the various current methods in the field of fault detection and identification. Secondly, the notion of artificial immune systems is to be introduced and explained. Finally, this thesis aims to investigate the performance of AIS on data gathered from simulated case studies both with and without noise. Three different methods of generating detectors are used to monitor various different processes for anomalous events. These are: (1) Random Generation of detectors, (2) Convex Hulls, (3) The Hypercube Vertex Approach. It is found that random generation provides a reasonable rate of detection, while convex hulls fail to achieve the required objectives. The hypercube vertex method achieved the highest detection rate and lowest false alarm rate in all case studies. The hypercube vertex method originates from this project and is the recommended method for use with all real valued systems, with a small number of variables at least. It is found that, in some cases AIS are capable of perfect classification, where 100% of anomalous events are identified and no false alarms are generated. Noise has, expectedly so, some effect on the detection capability on all case studies. The computational cost of the various methods is compared, which concluded that the hypercube vertex method had a higher cost than other methods researched. This increased computational cost is however not exceeding reasonable confines therefore the hypercube vertex method nonetheless remains the chosen method. The thesis concludes with considering AIS’s performance in the comparative criteria for diagnostic methods. It is found that AIS compare well to current methods and that some of their limitations are indeed solved and their abilities surpassed in certain cases. Recommendations are made to future study in the field of AIS. Further the use of the Hypercube Vertex method is highly recommended in real valued scenarios such as Process Engineering. | en_ZA |
| dc.format.extent | 5478210 bytes | en_ZA |
| dc.format.mimetype | application/pdf | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/1780 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | Stellenbosch : University of Stellenbosch | |
| dc.rights.holder | University of Stellenbosch | |
| dc.subject | Artificial intelligence -- Modelliing | en_ZA |
| dc.subject | Intelligent systems | en_ZA |
| dc.subject | Dissertations -- Process engineering | en |
| dc.subject | Theses -- Process engineering | en |
| dc.subject | Immune system -- Computer simulation | en |
| dc.subject | Computational intelligence | en |
| dc.subject | Evolutionary computation | en |
| dc.subject | Chemical process control | en |
| dc.subject | Fault location (Engineering) | en |
| dc.title | Diagnostic monitoring of dynamic systems using artificial immune systems | en_ZA |
| dc.type | Thesis | en_ZA |