Abstract: A computer implemented method of analyzing measurement results of a target system, such as an industrial process or communication network. The method includes receiving a data sample with a plurality of variables representing the measurement results, detecting that the data sample is an anomalous sample using an anomaly detection model, processing the data sample by applying an imputation model to selected subsets of variables of the data sample to obtain imputed samples, and applying the anomaly detection model to the imputed samples, determining anomalous variables of the data sample based on results from the processing of the data sample, and outputting the anomalous variables of the data sample for management operations in the target system.

Abstract: The present disclosure is directed to systems and methods for economically optimal control of an electrical system. A two-stage controller includes an optimizer and a high speed controller to effectuate a change to one or more components of the electrical system. The high speed controller receives a set of control parameters for an upcoming extended time period. The control parameters include a plurality of bounds for an adjusted net power of the electrical system. The high speed controller sets an energy storage system command control variable (ESS command) based on a state of adjusted net power of the electrical system and the set of control parameters.

Abstract: Methods and systems for training and implementing metrology recipes based on performance metrics employed to quantitatively characterize the measurement performance of a metrology system in a particular measurement application. Performance metrics are employed to regularize the optimization process employed during measurement model training, model-based regression, or both. For example, the known distributions associated with important measurement performance metrics such as measurement precision, wafer mean, etc., are specifically employed to regularize the optimization that drives measurement model training. In a further aspect, a trained measurement model is employed to estimate values of parameters of interest based on measurements of structures having unknown values of one or more parameters of interest. In a further aspect, trained measurement model performance is validated with test data using error budget analysis.

Abstract: A method for automating system identification includes performing a system identification experiment, and performing a system identifying processing by fitting a model to data from the system identification experiment. The method also includes performing model reduction to generate a model numerically suitable for controller synthesis by removing inconsequential states that cause controller optimization methods to fail. The method further includes performing control synthesis using the generated model or reduced models, including disturbance spectrum estimates, to generate a candidate controller design to be used during system operation. The method also includes checking for controller robustness using the identified model to ensure stability of the system while maximizing closed-loop bandwidth and performance.

Abstract: An object is to read the information of a feature shape included in a machining program so as to calculate a control command suitable for machining processing on the feature shape. A numerical controller (300) includes: a feature detection unit (302) which detects the feature of a machined shape from a machining program that commands the movement of a tool or a workpiece; an inward-turning amount calculation unit (303) which calculates, based on a servo parameter of a servo controller (400) that drives the tool or the workpiece, the feature of the machined shape detected from the machining program and a machining requirement that specifies a machining condition, a relation formula that determines the inward-turning amount of a machining path with respect to a program path; and a feedrate determination unit (304) which determines a feedrate that is optimized with the relation formula.

Abstract: A method of monitoring and controlling an industrial process includes capturing images of a non-sensible process condition of the industrial process with an image capture device, receiving the images of the non-sensible process condition with a computer system, and converting each image into a corresponding numeric value with a machine learning model stored in and operable by the computer system. The corresponding numeric value is then received and compared to a predetermined numeric value for the non-sensible process condition with a closed-loop controller, and an actuator operates with the closed-loop controller to adjust operation of the industrial process when the corresponding numeric value fails to match the predetermined numeric value or is outside of a defined threshold near the predetermined numeric value.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

Abstract: A numerically controlled machine tool has at least one movement axis and is connected to a numerical controller which includes a parts program. Movements of each movement axis are limited by maximum permissible axis dynamics. The parts program has a sequence of instructions for machining a workpiece which specify different maximum desired speeds for the machining of the workpiece which change abruptly over time. The numerical controller approximates the different maximum desired speeds which change abruptly over time with a desired speed profile that is continuous over time and has a profile of the maximum desired speeds which is also continuous over time. The numerical controller uses the continuous desired speed profile to calculate the desired values of an actual movement profile of the movements for each movement axis.

Abstract: This control device of a machine tool is provided with: a storage unit which stores in advance a nominal diameter and a nominal length of a blade part of a rotary tool; an image-capturing instruction unit which outputs an image-capturing instruction to an image-capturing device; a model creation unit which generates model data of the blade part of the tool on the basis of the nominal diameter and the nominal length stored in the storage unit, generates model data of a tool shank and holder on the basis of an image stored in the image-capturing device, and creates model data of the rotary tool on the basis of the generated model data of the blade part and the generated model data of the tool shank and holder.

Abstract: A micro grid system comprises a secondary energy source and a power controller. The secondary energy source is associated with a micro grid that includes a fixed or mobile facility, and the secondary energy source is configured to generate first DC power signal. The power controller is in communication with the secondary energy source and an electric grid, and configured to receive first AC power signal from the electric grid and the first DC power signal from the secondary energy source and output a second AC power signal to loads in communication with the power controller. The power controller comprises an AC to DC frequency converter configured to change frequency and/or voltage of the second AC power signal, a processor, and a memory configured to store instructions that, when executed, cause the processor to control the frequency converter to change the frequency and/or voltage of the second AC power signal.

Abstract: A micro grid system comprises a secondary energy source and a power controller. The secondary energy source is associated with a micro grid that includes a fixed or mobile facility, and the secondary energy source is configured to generate first DC power signal. The power controller is in communication with the secondary energy source and an electric grid, and configured to receive first AC power signal from the electric grid and the first DC power signal from the secondary energy source and output a second AC power signal to loads in communication with the power controller. The power controller comprises an AC to DC frequency converter configured to change frequency and/or voltage of the second AC power signal, a processor, and a memory configured to store instructions that, when executed, cause the processor to control the frequency converter to change the frequency and/or voltage of the second AC power signal.

Abstract: A micro grid system comprises a secondary energy source and a power controller. The secondary energy source is associated with a micro grid that includes a fixed or mobile facility, and the secondary energy source is configured to generate first DC power signal. The power controller is in communication with the secondary energy source and an electric grid, and configured to receive first AC power signal from the electric grid and the first DC power signal from the secondary energy source and output a second AC power signal to loads in communication with the power controller. The power controller comprises an AC to DC frequency converter configured to change frequency and/or voltage of the second AC power signal, a processor, and a memory configured to store instructions that, when executed, cause the processor to control the frequency converter to change the frequency and/or voltage of the second AC power signal.

Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.

Abstract: Systems, methods, and control units for diagnosis and life prediction of one or more electro-mechanical system are provided. One method includes receiving sensor data from a plurality of sensors associated with operation of the electro-mechanical system. The method includes determining at least one system response associated with at least one failure mode of the electro-mechanical system from the sensor data, wherein the sensor data is indicative of the at least one failure mode of the electro-mechanical system. The method further includes receiving at least one simulated response associated with the at least one failure mode of the electro-mechanical system, wherein the at least one failure mode is simulated on a system model of the electro-mechanical system.

Abstract: The speed and security of the monitoring the operation of a drive component is improved by transferring data relating to the drive component and/or to the operation of the drive component to a central IT infrastructure. Within the central IT infrastructure, the transferred data are associated with a first model of the drive component, and with a second model of at least one virtual component associated with the first model. An operating state of the drive component is determined from a correlation of the first and second models.

Abstract: Computer implemented methods and systems incorporate physics-based and/or chemistry-based constraints into a model of a chemical, physical, or industrial process. The model is derived from a representative dataset of the subject process. The constrained model provides predictions of process behavior that are guaranteed to be consistent with incorporated constraints such as mass balances, atom balances, and/or energy balances while being less computationally intensive than equivalent first principle models. The constrained model can be constructed by matrix multiplication, namely multiplying the solution of an unconstrained linear model by a matrix that enforces the constraints. Improved process control models result, as well as improved process modeling and simulation models result.

Abstract: A system and method are provided for determining a causal inference in a manufacturing process. During operation, the system can receive data associated with a processing system which includes a set of interconnected machines and an associated set of processes. The system can generate, based on the data, a graph indicating flows of outputs between the machines as part of the processes. The system can determine, based on a set of variables, one or more candidate clusters in the graph. The system can perform, based on one or more variables of interest, root cause analysis on the one or more candidate clusters by: applying an additive noise model to prune the one or more candidate clusters from the graph; and determining, based on the pruned graph, a candidate pathway likely to cause an issue in at least one process, thereby facilitating improved efficiency in the processing system.

Abstract: An electromechanical joining system that uses an output force or output torque for performing a joining method and includes an electrical drive connected for driving a screw drive and is configured for generating actual values of force or torque that are provided as input variables to a monitoring device. The system includes a sensor configured for measuring the course of the forces or torques over time during the joining method and for detecting additional measurement values that are supplied to the monitoring device as input variables. Wherein the monitoring device links the supplied actual values with the supplied additional measurement values to detect upcoming wear of a wear-prone component of the electromechanical joining system. A method for analyzing the status of the electromechanical joining system is also disclosed.

Abstract: A food production management system that manages a food production process group having a plurality of production processes is disclosed. The food production management system includes a first acquisition unit and an output unit. The first acquisition unit acquires throughputs of each of the plurality of production processes. The output unit outputs first information. The first information includes information representing the throughputs of each of the plurality of production processes.

Abstract: A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.