TY - CONF A1 - Friedrich, Alexander A1 - Heckel, Thomas A1 - Casperson, Ralf A1 - Zhang, Tianyun A1 - Olm, G. A1 - Islam, A. A1 - Simroth, A. T1 - The AIFRI Project - Artificial Intelligence For Rail Inspection N2 - The rails of modern railways face an enormous wear and tear from ever increasing train speeds and loads. This necessitates diligent non-destructive testing for defects of the entire railway system. Non-destructive testing of rail tracks is carried out by rail inspection trains equipped with ultrasonic and eddy current test devices. However, the evaluation of the gathered data is mainly done manually with a strong focus on ultrasonic data, and defects are checked on-site using hand-held testing equipment. Maintenance measures are derived based on these on-site findings. The aim of the AIFRI project (Artificial Intelligence For Rail Inspection) is to - increase the degree of automation of the inspection process, from the evaluation of the data to the planning of maintenance measures, - increase the accuracy of defect detection, - automatically classify detected indications into risk classes. These aims will be achieved by training a neural network for defect detection and classification. Since the current testing data is unbalanced, insufficiently labeled and largely unverified we will supplement fused, simulated eddy current and ultrasonic testing data in form of a configurable digital twin. T2 - PostDoc Day 2022 CY - Berlin, Germany DA - 03.11.2022 KW - Non-destructive testing KW - Artificial intelligence KW - Simulation KW - Eddy current KW - Ultrasound PY - 2022 AN - OPUS4-57240 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -