Diagnosis of large inspection datasets using a adaptive, learning system

Performing the diagnosis of technical plants results in most of the cases in analyzing huge amounts of unstructured sensor data. If additionally the gathered sensor measurements are noisy or partial faulty and the knowledge about the underlying system or plant is incomplete, than adaptive, learning methods are required in order to interpret the measurements automatically. This paper gives an overview about our diagnosis tool. Two classification kernels, the one based on hybrid, neural network and the other on support vector machines are compared. The paper focuses on the aspects of successful use of learning methods and human expert interactivity in analyzing unstructured data coming from industrial application.