Abstract: A monitoring apparatus and method for quality monitoring of a supplemented manufacturing process to a set of predefined manufacturing processes of industrial manufacturing includes: obtaining teacher models, providing an initial version of a student learning model and an initial version of a generator learning model, for each teacher model, training the generator learning model and a teacher specific student model to create data samples where the teacher model and teacher specific student learning models do not agree in their predictions, and adapting the current version of the student learning model based all trained teacher specific student models, customizing the adapted student model to the supplemented manufacturing process by training the adapted student model with annotated data of the supplemented manufacturing process, and monitoring the supplemented manufacturing process by processing the customized student model using data samples collected during the supplemented manufacturing process as input dat

Abstract: A monitoring apparatus for an industrial manufacturing scenario is provided, including a) providing an initial version of a prediction unit, b) obtaining a set of unlabeled input data samples, c) aggregating an embedding indicator, d) aggregating a value indicator for each data sample of the set of unlabeled input data samples, e) selecting a subset of data samples of the set of unlabeled input data samples depending on the value indicator and outputting a labelling request to a labelling unit, f) receiving labels for the subset of data samples in the labelling unit, g) training the current version of the prediction unit resulting in a trained version of the prediction unit, and h) outputting a monitoring result for the set of unlabeled input data samples (Ds) by the trained version of the prediction unit indicating the quality of the supplemented manufacturing to control the manufacturing process.

Abstract: A monitoring apparatus and method for quality monitoring of a supplemented manufacturing process to a set of predefined manufacturing processes of industrial manufacturing includes: obtaining teacher models, providing an initial version of a student learning model and an initial version of a generator learning model, for each teacher model, training the generator learning model and a teacher specific student model to create data samples where the teacher model and teacher specific student learning models do not agree in their predictions, and adapting the current version of the student learning model based all trained teacher specific student models, customizing the adapted student model to the supplemented manufacturing process by training the adapted student model with annotated data of the supplemented manufacturing process, and monitoring the supplemented manufacturing process by processing the customized student model using data samples collected during the supplemented manufacturing process as input dat

Abstract: Various embodiments of the teachings herein include a computer implemented sample preparation method for generating a new sample of data for augmenting simulation data to generate realistic data to be applied for training of a data evaluation model. The method may include generating the new sample based on an output data set sampled from a model of an input data set based on residual data. The residual data are based on real data of a real process and simulated data of a simulated process corresponding to the real process.

Abstract: A method and an apparatus for optimizing diagnostics of rotating equipment is provided. The apparatus includes a device for providing status information about status of the rotating equipment over a series of time windows whereby status can be derived from sensor features of at least one available sensor taking measurements during a predefinable time period, a device for using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, whereby status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window, device for providing an amount of textual diagnostic knowledge cases, device for extracting semantic information on text features from the textual diagnostic knowledge cases, and device for combining status information and semantic information into a unified representation enabling optimization of the diagnostics.

Abstract: A monitoring system for recognizing a contingency in a power supply network including in-field measurement devices adapted to generate measurement data of the power supply network and a processing unit adapted to process the measurement data generated by the in-field measurement devices of the power supply network by using a local network state estimation model to calculate local network state profiles used to generate a global network state profile, wherein the processing unit is further adapted to process the measurement data generated by the in-field measurement devices of the power supply network to provide a relevance profile including for the in-field measurement devices a relevance distribution indicating a probability where the origin of a contingency within the power supply network.

Abstract: Provided is a method and system for detection of anomalous work pieces that includes computing at least one deviation data signal for a target data signal of a target work piece with respect to reference data signals recorded for a corresponding production process step of a set of reference work pieces, performing a stepwise anomaly detection by data processing of the at least one computed deviation data signal and a process type indicator indicating a type of the production process step using a trained anomaly detection data model to calculate for each time step or path length step of the production process step an anomaly probability that the respective time step or path length step is anomalous, and classifying the target work piece and/or the production process step as being anomalous or not anomalous on the basis of the calculated anomaly probabilities.

Abstract: A monitoring system includes in-field measurement devices adapted to generate measurement data of a power supply network, and a processing unit adapted to process the measurement data using a local network state estimation model to calculate local network state profiles used to generate a global network state profile. The processing unit is adapted to process the measurement data to provide a relevance profile comprising, for the in-field measurement devices, a relevance distribution indicating a probability where an origin of a contingency within the power supply network resides. The processing unit is adapted to compute a similarity between a candidate contingency profile formed by the generated global network state profile and by the calculated relevance profile and reference contingency profiles stored in a reference contingency database of the monitoring system to identify a reference contingency profile having a highest computed similarity as a recognized contingency within the power supply network.

Abstract: A method for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece and/or product includes creating a learning model for at least one production process for the at least one workpiece and/or product. The learning model is trained and initialized using a meta-learning algorithm, and the learning model is calibrated using normalized data of the at least one production process for the at least one workpiece and/or product. Currently generated data of the at least one production process for at least one currently produced workpiece/product is forwarded to the learning model. The data is generated by sensors. The learning model compares the currently generated data with the normalized data and finds deviations. The learning model scales the deviations between the currently generated data and the normalized data, and the learning model communicates presence of an anomaly for the currently produced workpiece/product.

Abstract: The present disclosure relates to a method of and a system for building a latent feature extractor as well as a neural network including a latent feature extractor built by the method and/or with the system. The method includes providing non-uniform training data for a multitude of tasks and optimizing parameters of a neural network of the latent feature extractor based on the multitude of tasks.

Abstract: Provided is a method for machine learning of analytical models, AMs, including core model components, CMCs, shared between tasks, t, of different customers and including specialized model components, SMCs, specific to customer tasks, t, of individual customers, wherein the machine learning of the analytical models, AMs, is performed collaboratively based on local data, LD, provided by machines of the customer premises of different customers without the local data, LD, leaving the respective customer premises.

Abstract: A method for operating a network, such as an automation network, for example, has multiple node devices provided that are networked to one another. There is a global time available, and the node devices record their operating parameters. The operating parameters are allocated to a respective address element as content elements in order to be stored in a tensorial database structure. Control or adaptation of the operation of the network with its node devices and couplings is facilitated thereby. The method is suitable particularly for use in supply networks, automated production installations, communication networks, transport networks and logistical networks. The proposed storing allows easy visualisation, depiction and evaluation of operating states of the network and of its node devices.

Abstract: A method for identifying automatically an inner node within a hierarchical network causing an outage of a group of leaf nodes at the lowest hierarchical level, the method including providing an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level; decomposing the state matrix into a first probability matrix indicating for each inner node the probability that the inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network and evaluating the first probability matrix to identify the inner node having caused the outage of the group of leaf nodes.

Abstract: A monitoring system adapted to recognize a contingency in a power supply network, PSN, (2), the monitoring system (1) comprising in-field measurement devices (3) adapted to generate measurement data (MD) of said power supply network (2) and a processing unit (4) adapted to process the measurement data (MD) generated by the in-field measurement devices (3) of said power supply network (2) by using a local network state estimation model (LNSM) to calculate local network state profiles (LNSPs) used to generate a global network state profile (GNSP), wherein said processing unit (4) is further adapted to process the measurement data (MD) generated by the in-field measurement devices (3) of said power supply network (2) to provide a relevance profile (RP) comprising for the in-field measurement devices (3) a relevance distribution indicating a probability where the origin of a contingency within the power supply network, PSN, (2) resides, wherein the processing unit (4) is further adapted to compute a similarity be

Abstract: A method and apparatus for determining a network topology of a hierarchical network, the apparatus including: a memory unit to store an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level of the hierarchical network; and a processing unit to decompose the stored state matrix into a first probability matrix indicating for each inner node of the hierarchical network the probability that the respective inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network, wherein the decomposed second probability matrix is evaluated by the processing unit to determine the network topology.

Abstract: A method and apparatus for automatic recognition of similarities between perturbations in a network, the apparatus includes a memory unit for storing a first data array of multiple perturbation data snapshots each recorded in response to a perturbation observed in the network; a generation unit adapted to generate by machine learning a data model of perturbations trained on the first data array, wherein the trained data model provides a latent vector representation for each of the perturbations; a recording unit adapted to record a perturbation data snapshot if a perturbation is observed during operation of said network and adapted to provide a corresponding second data array for the recorded perturbation data snapshot; and a processing unit adapted to derive a latent vector representation for the observed perturbation from the second data array using the trained data model of perturbations.

Abstract: A method and an apparatus for optimizing diagnostics of rotating equipment, in particular a gas turbine is provided.

Abstract: Provided is a method and system for detection of anomalous work pieces that includes computing at least one deviation data signal for a target data signal of a target work piece with respect to reference data signals recorded for a corresponding production process step of a set of reference work pieces, performing a stepwise anomaly detection by data processing of the at least one computed deviation data signal and a process type indicator indicating a type of the production process step using a trained anomaly detection data model to calculate for each time step or path length step of the production process step an anomaly probability that the respective time step or path length step is anomalous, and classifying the target work piece and/or the production process step as being anomalous or not anomalous on the basis of the calculated anomaly probabilities.

Abstract: A method and apparatus for determining a network topology of a hierarchical network, the apparatus including: a memory unit to store an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level of the hierarchical network; and a processing unit to decompose the stored state matrix into a first probability matrix indicating for each inner node of the hierarchical network the probability that the respective inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network, wherein the decomposed second probability matrix is evaluated by the processing unit to determine the network topology.

Abstract: A monitoring system for recognizing a contingency in a power supply network including in-field measurement devices adapted to generate measurement data of the power supply network and a processing unit adapted to process the measurement data generated by the in-field measurement devices of the power supply network by using a local network state estimation model to calculate local network state profiles used to generate a global network state profile, wherein the processing unit is further adapted to process the measurement data generated by the in-field measurement devices of the power supply network to provide a relevance profile including for the in-field measurement devices a relevance distribution indicating a probability where the origin of a contingency within the power supply network.