Anomaly detection has been studied for many years and has been implemented successfully in many domains. There are various approaches one could adopt to achieve this goal. The core idea behind these is to build a model that is trained in detecting patterns of anomalies. For this thesis, the objective was to detect anomalies and identify the causes for the same given the data about the process in a manufacturing setup. The scenario chosen was of a ring rolling process followed at Ovako steel company in Hofors, Sweden. An approach involving tree ensemble method coupled with manual feature engineering of multivariate time series was adopted. Through the various experiments performed, it was found that the approach was successful in detecting anomalies with an accuracy varying between 79% to 82%. To identify the causes of anomalies, feature importance using Shapley additive explanation method was implemented. Doing so, identified one feature that was very prominent and could be the potential cause for anomaly. In this report, the scope for improvement and future work has also been suggested.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-18400 |
| Date | January 2019 |
| Creators | Alcaçoas, Dellainey |
| Publisher | Högskolan i Skövde, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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