Abstract: The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.

Abstract: Devices and methods for determining a product quality resulting from a manufacturing process are disclosed herein. In one example, the method includes simulatively determining one of a plurality of manufacturing state variables depending on a scattering of the manufacturing state variables, acquiring one of the manufacturing state variables by a sensor, and associatively determining the product quality depending on the manufacturing state variables.

Abstract: The disclosure relates to a method and a device for producing a product and to a computer program product. The product is produced in at least one production step. A quality control check is optionally carried out after at least one of the production steps to determine a quality index of the product in question. To save on the quality control check, a quality indicator of the product in question is determined using production data. The production data are advantageously provided by sensors. The quality indicator of the product in question may be calculated using an adaptive algorithm. The adaptive algorithm may be taught and/or improved using quality indices of a quality control unit and the corresponding production data. The adaptive algorithm may be taught with the aid of a further computing unit, in particular in a cloud.

Abstract: The invention relates to a method for monitoring a milling process of a printed circuit board, having the steps of: (a) detecting (S1) the rotational speed of a milling head (2) of a milling machine (1) and at least one other operating parameter of the milling machine (1) during the milling process, wherein the other operating parameter is an electric supply current for operating the milling machine, and (b) analyzing (S2) the detected rotational speed and the detected operating parameter using a trained adaptive algorithm for detecting anomalies during the milling process.

Abstract: The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.

Abstract: The disclosure relates to a method and a device for producing a product and to a computer program product. The product is produced in at least one production step. A quality control check is optionally carried out after at least one of the production steps to determine a quality index of the product in question. To save on the quality control check, a quality indicator of the product in question is determined using production data. The production data are advantageously provided by sensors. The quality indicator of the product in question may be calculated using an adaptive algorithm. The adaptive algorithm may be taught and/or improved using quality indices of a quality control unit and the corresponding production data. The adaptive algorithm may be taught with the aid of a further computing unit, in particular in a cloud.

Abstract: A similarity between a pattern distribution of selected state patterns of system state descriptions in a first set of system state descriptions and an average pattern distribution of the selected state patterns of the system state descriptions in a second set of system state description is determined. The similarity is compared with a plurality of such similarities between the pattern distributions within the second set of system state descriptions and the average pattern distribution in the second set of system state descriptions. An abnormal state of the system is determined using this comparison.

Abstract: In a method for the automated monitoring of a machine with a rotating machine component, at successive measurement times parameter values (Pt) of operating parameters of the machine and in each case over a predefined measurement duration, vibrations of the machine are recorded. A vibration spectrum (Yt(f)) of the vibrations recorded during the measurement duration is determined for each measurement duration. A mean power density (?t(i)) of the vibration spectrum (Yt(f)) is in each case determined from the vibration spectrum (Yt(f)) for a multiplicity of frequency ranges. During a learning phase a model function ({circumflex over (?)}(1)(P) to {circumflex over (?)}(N)(P)) for an averaged power density is determined for each frequency range as a function of the operating parameters.

Abstract: A similarity between a pattern distribution of selected state patterns of system state descriptions in a first set of system state descriptions and an average pattern distribution of the selected state patterns of the system state descriptions in a second set of system state description is determined. The similarity is compared with a plurality of such similarities between the pattern distributions within the second set of system state descriptions and the average pattern distribution in the second set of system state descriptions. An abnormal state of the system is determined using this comparison.

Abstract: An electrical energy supply network has plurality of electrical energy users and/or energy generators that are connected to a common control device. In order to operate as efficiently as possible, with newly added energy generators and energy users to be linked into the existing control system, at least two energy profile groups are provided. The energy users and/or energy generators are assigned to the groups such that energy users having the same or similar energy consumption time series and energy generators having the same or similar energy generation time series are assigned to the same energy profile group. A further energy user or energy generator is assigned to one of the energy profile groups and all energy generators and energy users of an energy profile group are operated in an identical manner by the control device.

Abstract: In a method for the automated monitoring of a machine with a rotating machine component, at successive measurement times parameter values (Pt) of operating parameters of the machine and in each case over a predefined measurement duration, vibrations of the machine are recorded. A vibration spectrum (Yt(f)) of the vibrations recorded during the measurement duration is determined for each measurement duration. A mean power density (?t(i)) of the vibration spectrum (Yt(f)) is in each case determined from the vibration spectrum (Yt(f)) for a multiplicity of frequency ranges. During a learning phase a model function ({circumflex over (?)}(1)(P) to {circumflex over (?)}(N)(P)) for an averaged power density is determined for each frequency range as a function of the operating parameters.

Abstract: An electrical energy supply network has plurality of electrical energy users and/or energy generators that are connected to a common control device. In order to operate as efficiently as possible, with newly added energy generators and energy users to be linked into the existing control system, at least two energy profile groups are provided. The energy users and/or energy generators are assigned to the groups such that energy users having the same or similar energy consumption time series and energy generators having the same or similar energy generation time series are assigned to the same energy profile group. A further energy user or energy generator is assigned to one of the energy profile groups and all energy generators and energy users of an energy profile group are operated in an identical manner by the control device.