A Visual Analytics Approach to Sensor Analysis for End-of-Line Testing

End-of-Line testing is the final step of modern production lines that assures the quality of produced units before they are shipped to customers. Automatically deciding between functional and defective units as well as classifying the type of defect are main objectives. In this thesis, a dataset consisting of three phase internal rotor engine simulations is used to outline opportunities and challenges of Visual Analytics for End-of-Line testing. At first the simulation data is visually analyzed to understand the influence of the simulation input parameters. Afterwards features are extracted from the signals using discrete Fourier transform (DFT) and discreteWavelet transform (DWT) to represent the different simulations. Principal Component Analysis (PCA) is applied to further reduce the dimensionality of the data to finally apply K-Means to cluster the datasets and also perform a classification using a support vector machine (SVM). It is discussed which methods are beneficial for the End-of-Line testing domain and how they can be integrated to improve the overall testing process.