Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods

Abstract:

The goal was to extract knowledge from data that is logged by the electronic system of

every 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, was

developed and applied to extract knowledge from data. The focus was on extracting

information 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 methods

used come from the field of unsupervised learning, a sub-field of machine learning and

include the methods self-organizing maps, multi-dimensional scaling and fuzzy c-means

clustering.

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 operating

environment regarding road condition or gross combination weight. The results showed

that there is relevant information in the logged data that describes these differences

in the operating environment. A third data-set consisted of populations with different

engine 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 two

configurations 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 experiments

to find such vehicles were conducted and recommendations for an automated

application were given.