Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods

The goal was to extract knowledge from data that is logged by the electronic system ofevery Volvo truck. This allowed the evaluation of large populations of trucks without requiring additional measuring devices and facilities.An evaluation cycle, similar to the knowledge discovery from databases model, wasdeveloped and applied to extract knowledge from data. The focus was on extractinginformation in the logged data that is related to the class labels of different populations,but also supported knowledge extraction inherent from the given classes. The methodsused come from the field of unsupervised learning, a sub-field of machine learning andinclude the methods self-organizing maps, multi-dimensional scaling and fuzzy c-meansclustering.The developed evaluation cycle was exemplied by the evaluation of three data-sets.Two data-sets were arranged from populations of trucks differing by their operatingenvironment regarding road condition or gross combination weight. The results showedthat there is relevant information in the logged data that describes these differencesin the operating environment. A third data-set consisted of populations with differentengine configurations, causing the two groups of trucks being unequally powerful.Using the knowledge extracted in this task, engines that were sold in one of the twoconfigurations and were modified later, could be detected.Information in the logged data that describes the vehicle's operating environment,allows to detect trucks that are operated differently of their intended use. Initial experimentsto find such vehicles were conducted and recommendations for an automatedapplication were given.

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