Automatic Defect Detection by Classifying Aggregated Vehicular Behavior

Detecting defects is a major task for all complex products, as automobiles. Current symptoms are the failure codes a vehicle produces and the complaints of a customer. An important part on the defect detection is the vehicular behavior. This paper highlights the analysis of vehicular data as a new symptom in the customer service process. The proposed concept combines the necessary preprocessing of vehicular data, especially the feature-based aggregation of this data, with the analysis on different sets of features for detecting a defect. In the modeling part a Support Vector Machine classifier is trained on single observed situations in the vehicular behavior and a Decision Tree is used to abstract the model output to a trip decision. The evaluation states a detection quality of 0.9418 as the F1-score.