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Active condition monitoring of automation systems is a strongly emerging topic. In this paper a purely data-driven approach is presented which allows for detection of wear-out or faulty situations in machine subsystems, each consisting of a servomotor driving different parts of the machine. The approach is based on empirical analysis of the motor's electric currents, but can be applied to the tracking errors of the servo controller as well. The features gained from the spectra of the motor currents or tracking errors, respectively, are reduced by singular value decomposition and classified by a kernel density based algorithm. Because of the purely data-driven approach the method can also be applied to a variety of different systems.

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.

Design of micro systems, MEMS or mechatronic systems is dominated by the interaction of effects from different physical domains. One important approach to decrease the number of design cycles significantly is system level modeling and simulation. The main challenges for the efficient use of modeling and simulation are a systematic approach for behavioral modeling, automated model generation as well as powerful simulation frameworks. Especially for the latter flexible handling of different models of computation is crucial.

We report about a novel Laser-Doppler velocity profile sensor with high spatial resolution in the micrometer range. The sensor can be used for applications in fluid mechanics, e.g. flow measurements. Miniaturized sensors are often necessary to measure outside the laboratory. One approach utilizes a diffractive optical element (DOE) in order to reduce the number of optical elements. Another approach utilizes a frequency division multiplexing, where the frequency of multiple laser diode beams are controlled and superposed. Both approaches are discussed and compared.

Excitation spectrum of homogeneously magnetized Ni/sub 80/Fe/sub 20/ rectangles using Brillouin light scattering (BLS) is reported. A model to qualitatively describe the spin-wave spectrum is presented and its results is compared with both experiment and micromagnetic simulations. Micromagnetic simulation show that the internal field is inhomogeneous along the direction of the applied field. It was concluded that the spin wave modes can be approximately described as a linear combination of the modes of a longitudinally magnetized stripe and a transversely magnetized stripe.