Edit StudentProjectForm: Anomaly Detection on Truck Histograms

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Summary:	MANDATORY: (provide a one-line* summary of the project)*
Programme:	OPTIONAL: (Name and the number of credits)
Keywords:	OPTIONAL: (Give 4-5 keywords)
TimeFrame:	MANDATORY: (Indicate time for the project, for example, Fall 2024)
References:	Learning Low-Dimensional Representation of Bivariate Histogram Data https://ieeexplore.ieee.org/abstract/document/8464276 OPTIONAL: (Give 2-3 references for students to start with)
Prerequisites:	Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; python programming skills for implementing machine learning algorithms OPTIONAL: (Courses, any other important information)
Supervisor(s):	MANDATORY: (can be multiple names, coma-separated)
Examiner:	OPTIONAL: (Name of project Examiner)
Author(s):	OPTIONAL: (can be multiple names, coma-separated)
Level:	MANDATORY: choose one, preferably <Master>
Status:	MANDATORY: choose <Open> when the topic is ready for students to select

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There are many bivariate histograms collected on-board of Volvo Trucks which accumulate information about the operation of the vehicles through time. Measuring the number of hours that a vehicle has a certain speed versus torque could be one example of these histograms. Early detection of anomalous behaviour could be important for improving safety and uptime. Anomalies behaviour can be caused by wear, failure or malfunctions of components in a vehicle. It can also be caused by usage pattern through life time of a vehicle. This thesis will investigate if a vehicle has been used as usual or in anomalous way by analysing bivariate histogram data. The comparison can be done based on history of an individual vehicle or group of vehicles.

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Edit StudentProjectForm: Anomaly Detection on Truck Histograms

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