Sensorless drive diagnosis using automated feature extraction, significance ranking and reduction

Systems for process automation become increasingly complex and also tend to be composed of autonomous subsystems, which is strongly driven by the progress made in information technology. An active field of research is the implementation of monitoring and control at sub-system level using cognitive approaches. In this paper we present a method for autonomous and sensorless condition monitoring of an electric drive train. Based on experiment design we measured phase currents of a physical demonstrator device including mechanical defects and extracted signal features using proper orthogonal decomposition. In favor of classification of different defect states we performed a linear discriminant analysis, which yields appropriate data for a Fuzzy-Pattern-Classification algorithm. As a result we were able to identify different reference defect states as well as previously unknown states.