Abstract: A method for monitoring a condition of a system or process includes acquiring sensor data from a plurality of sensors disposed within the system (S41 and S44). The acquired sensor data is streamed in real-time to a computer system (S42 and S44). A discriminative framework is applied to the streaming sensor data using the computer system (S43 and S45). The discriminative framework provides a probability value representing a probability that the sensor data is indicative of an anomaly within the system. The discriminative framework is an integration of a Kalman filter with a logistical function (S41).

Abstract: A generalized pattern recognition is used to identify faults in machine condition monitoring. Pattern clusters are identified in operating data. A classifier is trained using the pattern clusters in addition to annotated training data. The operating data is also used to cluster the signals in the operating data into signal clusters. Monitored data samples are then classified by evaluating confidence vectors that include substitutions of signals contained in the training data by signals in the same signal clusters as the signals contained in the training data.

Abstract: A machine fault diagnosis system is provided. The system combines a rule-based predictive maintenance strategy with a machine learning system. A simple set of rules defined manually by human experts is used to generate artificial training feature vectors to portray machine fault conditions for which only a few real data points are available. Those artificial training feature vectors are combined with real training feature vectors and the combined set is used to train a supervised pattern recognition algorithm such as support vector machines. The resulting decision boundary closely approximates the underlying real separation boundary between the fault and normal conditions.

Abstract: A generalized pattern recognition is used to identify faults in machine condition monitoring. Pattern clusters are identified in operating data. A classifier is trained using the pattern clusters in addition to annotated training data. The operating data is also used to cluster the signals in the operating data into signal clusters. Monitored data samples are then classified by evaluating confidence vectors that include substitutions of signals contained in the training data by signals in the same signal clusters as the signals contained in the training data.

Abstract: A method for monitoring operation behavior of a component of an industrial plant is provided. Parameter identifiers of operation parameters of the industrial plant are read from an element of a control system of the plant. Some of these parameter identifiers are selected and the selected parameter identifiers are forwarded to an element of the control system. Further, a time of a specified operation of the component is defined and behavior of the selected parameters of the component in the defined time is used as guide model for monitoring selected parameters of the component during component operation.

Abstract: A machine fault diagnosis system is provided. The system combines a rule-based predictive maintenance strategy with a machine learning system. A simple set of rules defined manually by human experts is used to generate artificial training feature vectors to portray machine fault conditions for which only a few real data points are available. Those artificial training feature vectors are combined with real training feature vectors and the combined set is used to train a supervised pattern recognition algorithm such as support vector machines. The resulting decision boundary closely approximates the underlying real separation boundary between the fault and normal conditions.

Abstract: The present invention relates to a device and a procedure for the withdrawal, manufacture, storage and transportation of blood products in the widest sense, in particular a closed blood removal, processing and storages system, as well as a system suitable for the administration of medicinal products which are obtained from blood or blood products, and a corresponding procedure for the legally conformant and GMP-compliant preparation, manufacture, storage and transportation of blood products or blood components or blood constituents or blood preparations (within the meaning of the Medical Products Act AMG). The present invention in particular allows the manufacture, storage, transportation and administration of completely aliquoted autologous serum eye drops for direct application to the patient or other drugs manufactured from blood or blood products without the need for clean room laboratories.

Abstract: A method for monitoring operation behaviour of a component of an industrial plant is provided. Parameter identifiers of operation parameters of the industrial plant are read from an element of a control system of the plant. Some of these parameter identifiers are selected and the selected parameter identifiers are forwarded to an element of the control system. Further, a time of a specified operation of the component is defined and behaviour of the selected parameters of the component in the defined time is used as guide model for monitoring selected parameters of the component during component operation.