Siemens Aktiengesellschaft

Siegmund Düll

Steffen Udluft

Volkmar Sterzing

Alexander Hentschel

Clemens Otte

Andreas Graichen

Marc Christian Weber

Jonas Eriksson

Kai Heesche

Jerry Fornander

Felix Buggenthin

Mitchell Joblin

Axel Reitinger

Michael Caelers

Vincent Sidenvall

Victor Balanica

Jatinder P Singh

Per Egedal

Thomas Esbensen

Gurdev Dave Singh

Hans-Gerd Brummel

Raymond Scott Nordlund

Thomas A Runkler

Markus Michael Geipel

Michael Müller

Daniel Hein

Glenn E Sancewich

Sunil Viswanathan

Hany F Bassily

Jose L Alonso

Alexander Hans

Kristian Robert Dixon

Yi Huang

Dieter Bogdoll

Jean-Christoph Heyne

Lina Weichbrodt

A technical system controller is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method. The training data includes state data, which specifies states of the technical system, and control action data, which specifies control actions of the technical system. A chronological sequence of control action data is extracted specifically from the training data and is checked for a change over time. If a change over time is ascertained, a time window including the change is ascertained, and training data which can be found within the time window is extracted in a manner which is specific to the time window. The controller is then trained by the machine learning method using the extracted training data and is thus configured for controlling the technical system.

Controller for controlling a technical system, and method for configuring the controller

An apparatus and method for monitoring a quality of an object of a 3D-print job series of identical objects, each object built from a multitude of stacked 2D-layers printed by a 3D-printer in an additive manufacturing process, including: determining a layer quality indicator of a currently printed layer of an object, comparing the determined layer quality indicator of the currently printed layer with a predetermined lower confidence limit of the layer, the predetermined lower confidence limit being calculated depending on layer quality indicators of previously completely manufactured objects complying with predefined quality requirements, and generating a warning signal, if the layer quality indicator of the currently printed layer has a value equal or lower than the lower quality limit is provided.

Method and apparatus for monitoring a quality of an object of a 3D-print-job series of identical objects

For reducing oscillations in a technical system plurality of different controller settings for the technical system is received. For a respective controller setting signal representing a time series of operational data of the technical system controlled by the respective controller setting is received, the signal is processed, whereby the processing includes a transformation into a frequency domain, and an entropy value of the processed signal is determined. Depending on the determined entropy values a controller setting from the plurality of controller settings is selected, and the selected controller setting is output for configuring the technical system.

Method, controller, and computer product for reducing oscillations in a technical system

Provided is a method for computer-aided processing of quality information of an object manufactured by stacked printed layers in an additive manufacturing system, including the steps of: receiving a quality indicator for each printed layer of the object from the manufacturing system, assigning a color out of a predefined set of colors to each quality indicator depending on the value of the quality indicator, visualizing the quality indicators of the received manufactured layers as a sequence of colored bars ordered according to the sequence of the manufactured layers the color of each bar indicating the value of the quality indicator of the respective printed layer on a graphical user interface.

Method for computer-aided processing of quality information of an object and a respective assistance apparatus

In order to control a technical system by means of control model a data container is received, in which data container a control model having a training structure and model type information are encoded over all the model types. One of multiple model-type specific execution modules is selected for the technical system as a function of the model type information. Furthermore, operating data channels of the technical system are assigned input channels of the control model as a function of the model type information. Operating data of the technical system are acquired via a respective operating data channel and are transferred to the control model via an input channel assigned to this operating data channel. The control model is executed by means of the selected execution module, wherein control data are derived from the transferred operating data according to the training structure and are output to control the technical system.

Method and device for controlling a technical system by means of control models

A method of controlling a complex system and a gas turbine being controlled by the method are provided. The method comprises providing training data, which training data represents at least a portion of a state space of the system; setting a generic control objective for the system and a corresponding set point; and exploring the state space, using Reinforcement Learning, for a control policy for the system which maximizes an expected total reward. The expected total reward depends on a randomized deviation of the generic control objective from the corresponding set point.

Randomized reinforcement learning for control of complex systems

For controlling a target system, e.g. a gas or wind turbine or another technical system, a pool of control policies is provided. The pool of control policies comprising a plurality of control policies and weights for weighting each of the plurality of control policies are received. The plurality of control policies is weighted by the weights to provide a weighted aggregated control policy. With that, the target system is controlled using the weighted aggregated control policy, and performance data relating to a performance of the controlled target system are received. Furthermore, the weights are adjusted on the basis of the received performance data to improve the performance of the controlled target system. With that, the plurality of control policies is reweighted by the adjusted weights to adjust the weighted aggregated control policy.

Controlling a target system

The present invention provides an enhanced setup of a 3D-printing device, especially to a laser powder bed fusion 3D-printing device. It is for this purpose, that data relating to previously printed products are stored in a database. When a new product is to be printed, the features of the new product are matched with features of previously printed products stored in the database. Accordingly, a suggestion for setting-up the 3D-printing device based on corresponding previously printed products and their setup parameters can be automatically determined and applied to the 3D-printing device.

Method and apparatus for controlling a 3D-printing device and 3D-printing device

An interactive assistance system and method for computer-aided control optimization for a technical system is provided. For example, a gas or wind turbine, in particular for optimizing the action sequence or the control variables of the plant (e.g. gas supply, compression), wherein an input terminal is provided for reading at least one status parameter providing a first system status of the technical system, and at least one setting parameter for adapting a reward function. A simulation module having a pre-trained neuronal network, simulating the plant, serves to simulate an action sequence on the technical system, starting from the first system status and to the prediction of the resulting statuses of the technical system.

Method for computer-aided installation control optimization using a simulation module

The embodiments relate to a method for the computer-aided control and/or regulation of a technical system, particularly a power generation installation. The actions to be performed in the course of regulation or control are ascertained using a numerical optimization method (e.g., a particle swarm optimization). In this case, the numerical optimization method uses a predetermined simulation model that is used to predict states of the technical system and, on the basis thereof, to ascertain a measure of quality that reflects an optimization criterion for the operation of the technical system.

Computer-aided control and/or regulation of a technical system

The invention concerns a method for the computerized control and/or regulation of a technical system (T). Within the context of the method according to the invention, there is implemented in a preset regulating process (CO, CO) an exploration rule (EP) by means of which new, as yet unknown, states (x) of the technical system (T) are started, a simulation model (SM) of the technical system (T) checking whether the actions (a) of the exploration rule (EP) lead to sequential states (x') lying within predetermined thresholds. Only in that case is the corresponding action (a) performed according to the exploration rule (EP) on the technical system. The method according to the invention enables new states to be explored within the framework of the operation of a technical system, it being ensured through checking of appropriate thresholds that the exploration is carried out imperceptibly and does not lead to incorrect operation of the technical system. The method according to the invention is suitable in particular for controlling or regulating gas turbines or wind turbines.

Method for the computerized control and/or regulation of a technical system

A computer-implemented method for controlling and/or regulating a technical system, in which actions to be carried out on the technical system are first of all determined using an action selection rule which was determined through the learning of a data-driven model and, in particular, a neural network. On the basis of these actions a numerical optimization searches for actions which are better than the original actions according to an optimization criterion. If such actions are found, the technical system is regulated or controlled on the basis of these new actions, such that the corresponding actions are applied to the technical system in succession. The method is suitable, in particular, for regulating or controlling a gas turbine, wherein the actions are preferably optimized with respect to the criterion of low pollutant emission or low combustion chamber humming.

Method for controlling and/or regulating a technical system in a computer-assisted manner

The invention concerns a method for the computerized control and/or regulation of a technical system. Within the context of the method according to the invention, an action-selection rule (PO') is determined which has a low level of complexity and yet is well suited to the regulating and/or control of the technical system, there being used for determination of the action-selection rule (PO&#x2032;) an evaluation measure (EM) which is determined on the basis of a distance measure and/or a reward measure and/or an action-selection rule evaluation method. The action-selection rule is then used to control and/or regulate the technical system. The method according to the invention has the advantage of the action-selection rule being comprehensible to a human expert. Preferably, the method according to the invention is used for regulating and/or controlling a gas turbine and/or a wind turbine.

A method for controlling a turbine is proposed, which is characterized at any point in the control by a hidden state. The dynamic behavior of the turbine is modeled with a recurrent neural network comprising a recurrent hidden layer. In this case, the recurrent hidden layer is formed from vectors of neurons, which describe the hidden state of the turbine at the time points of the regulation, wherein two vectors are chronologically linked for each time point with a first connection bridging a time and second connection bridging at least two points in time. Short-term effects can be controlled by means of the first connections and long-term effects can be adjusted by means of the second connections. Secondly, emissions and also occurring dynamics in the turbine can be minimized. Furthermore, a regulating device and a turbine with such a regulating device are proposed.

Controlling a turbine with a recurrent neural network

A method for computer-assisted modeling of a technical system is disclosed. At multiple different operating points, the technical system is described by a first state vector with first state variable(s) and by a second state vector with second state variable(s). A neural network comprising a special form of a feed-forward network is used for the computer-assisted modeling of said system. The feed-forward network includes at least one bridging connector that connects a neural layer with an output layer, thereby bridging at least one hidden layer, which allows the training of networks with multiple hidden layers in a simple manner with known learning methods, e.g., the gradient descent method. The method may be used for modeling a gas turbine system, in which a neural network trained using the method may be used to estimate or predict nitrogen oxide or carbon monoxide emissions or parameters relating to combustion chamber vibrations.

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Munich, Germany

Siegmund Düll