Exploring unsupervised anomaly detection in Bill of Materials structures.

• Main Authors: Lindgren, Erik, Allard, Niklas
• Format: Others
• Language: English
• Published: Linköpings universitet, Institutionen för datavetenskap 2019
• Subjects: Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160262

Siemens produce a variety of different products that provide innovative solutions within different areas such as electrification, automation and digitalization, some of which are turbine machines. During the process of creating or modifying a machine, it is vital that the documentation used as reference is trustworthy and complete. If the documentation is incomplete during the process, the risk of delivering faulty machines to customers drastically increases, causing potential harm to Siemens. This thesis aims to explore the possibility of finding anomalies in Bill of Material structures, in order to determine the completeness of a given machine structure. A prototype that determines the completeness of a given machine structure by utilizing anomaly detection, was created. Three different anomaly detection algorithms where tested in the prototype: DBSCAN, LOF and Isolation Forest. From the tests, we could see indications of DBSCAN generally performing the best, making it the algorithm of choice for the prototype. In order to achieve more accurate results, more tests needs to be performed.