IDSS for Predictive Quality Assurance

The aim of the Experiment is to implement a system able to support the operator in the identification of defects and anomalies / faulty pieces. The resolution of technical problems and the automatic update of failure dataset can also have a considerable impact on the overall production planning. In the age of Industry 4.0, machine and deep learning has attracted increasing interest for various research applications. In recent years, such AI models have been extensively implemented in fault detection and diagnosis systems. The machine architecture’s automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis systems. The latter rely on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of machine/ deep learning comes with challenges and costs.