A method for determining and/or verifying a status of a door system, wherein the door system has at least one first subsystem and one second subsystem, includes the following steps determining at least one item of measurement information relating to the door system is determined in a measuring step, and determining and/or verifying the status of the door system using a model and using the at least one determined item of measurement information in a status determination step, wherein the model has a first model block for the first subsystem and a second model block for the second subsystem.

A control system for an operable system such as a flow control system or temperature control system. The system operates in a control loop to regularly update a model with respect at least one optimizable input variable based on the detected variables. The model provides prediction of use of the input variables in all possible operation points or paths of the system variables which achieve an output setpoint. In some example embodiments, the control loop is performed during initial setup and subsequent operation of the one or more operable elements in the operable system. The control system is self-learning in that at least some of the initial and subsequent parameters of the system are determined automatically during runtime.

The application relates to an oscillation active damping control method and system for grid-tied type-4 wind turbine generator. The method comprises: based on an interconnection model of multiple subsystems, constructing a stored energy function and a dissipated energy function of a current inner loop control subsystem, and interaction energy functions between the current inner loop control subsystem and other subsystems are constructed, then establishing an energy feedback model of Type-4 wind turbine generator; when the oscillation occurs, obtaining instantaneous angular frequency of the PLL, and then based on the energy feedback model, adjusting the current reference value of the q-axis current inner loop generated by the reactive power outer loop control subsystem, to make the stored energy function decrease with time, so as to suppress the oscillation.

An information processing system according to an embodiment includes edge terminals, an information processing apparatus, and one or more service providing apparatuses. The edge terminals each transmit, to the information processing apparatus, monitoring data indicating a state of a target system to be analyzed. The information processing apparatus performs an analysis process by using an analysis mod& that inputs an input value including the monitoring data transmitted from the edge terminals and outputs an output value of a value function. The analysis process is a process to obtain the output value in response to the input value, The information processing apparatus transmits, to the service providing apparatuses, information indicating an analysis result of the analysis process. The service providing apparatuses each output information obtained by visualizing the analysis result on the basis of information indicating the analysis result.

Methods, apparatus, systems and articles of manufacture are disclosed to improve boundary excursion detection. An example apparatus to improve boundary excursion detection includes a metadata extractor to parse a first control stream to extract embedded metadata, a metadata label resolver to classify a boundary term of the extracted embedded metadata, a candidate stream selector to identify candidate second control streams that include a boundary term that matches the classified boundary term of the first control stream, and a boundary vector calculator to improve boundary excursion detection by calculating a boundary vector factor based on respective ones of the candidate second control streams that include the classified boundary term.

A method includes obtaining data associated with operation of a model-based industrial process controller. The method also includes identifying at least one estimated impact of at least one operational problem of the industrial process controller, where each estimated impact is expressed in terms of a lost opportunity associated with operation of the industrial process controller. The method further includes presenting the at least one estimated impact to a user. The at least one estimated impact could include impacts associated with noise or variance in process variables used by the industrial process controller, misconfiguration of an optimizer in the industrial process controller, one or more limits on one or more process variables, a quality of at least one model used by the industrial process controller, a quality of one or more inferred properties used by the industrial process controller, or one or more process variables being dropped from use by the industrial process controller.

To improve prediction accuracy of a process including a reaction in a chemical plant. A prediction apparatus includes a process data processing unit that performs a predetermined processing process on process data obtained from a chemical plant, and a prediction model generation unit that generates a prediction model having learned features of the process data obtained from the chemical plant, on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data among the process data obtained from the chemical plant or the process data processed by the process data processing unit. The first process data is used as an explanatory variable. The second process data or the value corresponding to the second process data is used as a response variable. Furthermore, the process data processing unit obtains a value corresponding to a reaction rate of a processing target in a predetermined period by using the process data.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive an event from an event source, the event comprising data and a timestamp. The building system operates to identify first contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building, enrich the event with the first contextual data, and provide the enriched event to a consuming system, the consuming system generating an output event based on the enriched event. The building system operates to identify second contextual data describing the output event in the digital twin, enrich the output event with the second contextual data, and provide the enriched output event to the consuming system or another consuming system.

Methods and systems of evaluating a performance of an entity are described. A processor may obtain first data indicating tier attributes of resources of the entity, second data indicating function attributes of the resources, and third data indicating productivity attributes of the resources. The processor may train a model based on the first data, the second data, and the third data, the model may represent transitions of the resources over time. The processor may receive a set of controls including at least an objective to optimize a performance of the entity. The processor may generate a controlled model by integrating the set of controls into the model. The processor may determine a set of outcomes from the controlled model that includes at least a set of transitions relating to the resources that may optimize the performance of the entity.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.