This system identification method includes: a step (S1) for measuring frequency responses (ω, H.sub.R1), (ω, H.sub.R2) . . . , and (ω, H.sub.Rn) in a real system under n sets of disturbances of different magnitudes; a step (S3) for calculating frequency responses (ω, H.sub.M1), (ω, H.sub.M2) . . . , (ω, H.sub.Mn) from input to output in n sets of mechanical models M1 to Mn including i sets (i is an integer of 1 or greater) of common parameters that do not change due to disturbance and j sets of disturbance variable parameters that do change due to disturbance; a step (S4) for calculating the values of a total of n sets of evaluation functions F (H.sub.Rk, H.sub.Mk) and the sum σF thereof, and steps (S3 to S6) for searching for the values of i sets of common parameters and j×n sets of disturbance variable parameters for which the sum σF would meet convergence conditions.

A device and method for determining a parameter of a lubricating method or of a lubricant. At least one input variable for a model is provided. The parameter is determined as a function of the model. The model encompasses a module which determines the parameter as a function of the at least one input variable. The model is trained as a function of input data which encompass data sets of the at least one input variable and an assignment of each of the data sets to a setpoint parameter. As a function of a comparison of a parameter determined for one of the data sets to the setpoint parameter assigned to this data set, either the model is continued to be trained, or a modified model is determined and the modified model being trained.

Methods for ascertaining a control strategy for a technical system using a Bayesian optimization method. The control strategy is created based on model parameters of a control model and is executable. The method includes providing a quality function whose shape corresponds to a regression function and that evaluates a quality of a controlling of the technical system based on model parameters; carrying out a Bayesian optimization method based on the quality function in order to iteratively ascertain a model parameter set having model parameters within a model parameter domain that indicates the permissible value ranges for the model parameters; and determining the model parameter domain for at least one of the model parameters as a function of an associated maximum a posteriori estimated value of the quality function.

In a method for training a model for an electric machine controlled by a control device, a temporal series of measured values that describe an operating variable of the electric machine is received by a training system. An untrained model embodied as an artificial neural network is then trained with the received measured values to produce a trained model. Control variables that describe the control device are determined with the trained model. The training system then receives a temporal series of measured values of a further electric machine that is different from the electric machine and controlled by a further control device. The trained model is then trained further with the computing facility using measured values of the further electric machine to produce a further trained model. The trained model and the further trained model is outputted via a second interface of the training system.

A method, node, and computer-readable medium are provided to convert a proprietary model to a tool-agnostic surrogate model using a functional mockup interface (FMI) standard. A proprietary model is received as a functional-mockup unit (FMU) An automated dataset generation is performed on the FMU to create input/output datasets based on design of experiments and input requirements. Steady-state operational-points are determined. The tool-agnostic surrogate model is generated based on the input/output datasets and the steady-state operational-points. The tool-agnostic surrogate model is output as an output FMU model that is free of licensing requirements of a license for the proprietary model. The tool-agnostic surrogate model may be a steady-state surrogate model, a dynamic surrogate model, or a combination thereof.

A method includes obtaining operating data associated with operation of a cross-directional industrial process controlled by at least one model-based process controller. The method also includes, during a training period, performing closed-loop model identification with a first portion of the operating data to identify multiple sets of first spatial and temporal models. The method further includes identifying clusters associated with parameter values of the first spatial and temporal models. The method also includes, during a testing period, performing closed-loop model identification with a second portion of the operating data to identify second spatial and temporal models. The method further includes determining whether at least one parameter value of at least one of the second spatial and temporal models falls outside at least one of the clusters. In addition, the method includes, in response to such a determination, detecting that a mismatch exists between actual and modeled behaviors of the industrial process.

Techniques for converting an initial control program code version to a new control program code version are disclosed herein. In at least one implementation, input and output states of an industrial controller are monitored while the industrial controller executes the initial control program code version to operate a machine system and functional design specification for the industrial controller is generated. An instruction set of the industrial controller is converted into a new instruction set for a new industrial controller, and one or more equivalent instructions in the new instruction set that are equivalent to instructions in the instruction set of the industrial controller are identified. The new control program code version is generated based on at least the functional design specification and the one or more equivalent instructions in the new instruction set that are equivalent to the instructions in the instruction set of the industrial controller.

Estimating energy generated by a solar system in a predetermined geographic area comprises, at each predetermined time instant: retrieving measured values of at least one weather parameter and of solar irradiance in the geographic area, the values related to a time slot before the predetermined time instant; performing auto-regression analysis of the measured values; estimating, based on the auto-regression analysis, a relationship between the at least one weather parameter and the solar irradiance; retrieving forecasted values of the at least one weather parameter in the geographic area, the forecasted values being forecasted for a second time slot after the predetermined time instant; performing regression analysis of the relationship between the at least one weather parameter and the solar irradiance of the forecasted values; forecasting solar irradiance in the second time slot based on the regression analysis, and estimating energy generated by the solar system in the second time slot.

Techniques to facilitate generation of controller application code that emulates functionality of an industrial controller are disclosed herein. In at least one implementation, a computing system interfaces with the industrial controller and monitors input and output states of the industrial controller while the industrial controller operates a machine system. The input and output states of the industrial controller used to operate the machine system are analyzed to generate a functional design specification for the industrial controller. The controller application code that emulates the functionality of the industrial controller is generated based on the functional design specification.

A method includes repeatedly identifying one or more values for one or more model parameters of at least one model associated with a process. The one or more values for the one or more model parameters are identified using data associated with the process. The method also includes clustering the values of the one or more model parameters into one or more clusters. The method further includes identifying one or more additional values for the one or more model parameters using additional data associated with the process. In addition, the method includes detecting a mismatch between the at least one model and the process in response to determining that at least some of the one or more additional values fall outside of the one or more clusters. The values could be clustered using a support vector machine.