Master’s Thesis: Machine Learning for Anomaly Detection in Heavy Equipment
About this role
Master’s Thesis: Machine Learning for Anomaly Detection in Heavy Equipment
Eskilstuna, SE, 405 08
Background
Volvo Construction Equipment (VCE) designs and manufactures machines for a wide range of industries, including construction, mining, and forestry. Equipment such as wheel loaders, articulated haulers, and excavators is used in demanding operating environments, where breakdowns can be costly and must therefore be minimized.
VCE is investigating methods to improve the reliability and robustness before the machines reach customers. In this thesis, you will contribute by developing machine learning algorithms to identify anomalous behaviour during system-level testing.
Thesis Description
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