The system generally includes a crosspoint switch in a local data collection system having multiple inputs and multiple outputs including a first input connected to a first sensor and a second input connected to a second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of a first sensor signal and a second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal and the second sensor signal. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history. The local data collection system is configured to manage data collection bands.

The system generally includes a crosspoint switch in a local data collection system having multiple inputs and multiple outputs including a first input connected to a first sensor and a second input connected to a second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of a first sensor signal and a second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal and the second sensor signal. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history. The local data collection system is configured to manage data collection bands.

The system generally includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. Unassigned outputs are configured to be switched off producing a high-impedance state. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history of a data acquisition unit in which the onboard card set is located. The local data collection system is configured to manage data collection bands.

A control device includes feature amount generating means for generating a feature amount suitable for detecting an anomaly that occurs in a control target from data that relates to the control target, machine learning means for carrying out machine learning using the feature amount generated by the feature amount generating means, anomaly detecting means for detecting the anomaly, based on the feature amount generated by the feature amount generating means and an anomaly detection parameter determined based on a learning result of the machine learning and used in detection of the anomaly that occurs in the control target, instructing means for instructing the anomaly detecting means to perform detection of the anomaly, and data compressing means for data-compressing the feature amount generated by the feature amount generating means, and providing the data-compressed feature amount to the machine learning means and the anomaly detecting means.

A computer-implemented method for predicting material properties in an Additive Manufacturing (AM) process is provided. The method comprises: receiving sensor data during the build of a metallic component using the AM process wherein the sensor data includes time-series temperature data of a surface of the metallic component recorded by a photodiode and time-series temperature data of a surface of the metallic component recorded by a pyrometer; receiving ICME (Integrated Computational Materials Engineering) model output data for building the component wherein the ICME model output data includes predicted melt pool dimensions time-series data, predicted melt temperature time-series data, and predicted defects forming as a result of melt pool evolution and movement; and estimating using the received sensor data and the received ICME model output data one or more material properties associated with the metallic component using a material property prediction module configured to predict one or more of the material properties.

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.

A computer-implemented method for predicting material properties in an Additive Manufacturing (AM) process is provided. The method comprises: receiving sensor data during the build of a metallic component using the AM process wherein the sensor data includes time-series temperature data of a surface of the metallic component recorded by a photodiode and time-series temperature data of a surface of the metallic component recorded by a pyrometer; receiving ICME (Integrated Computational Materials Engineering) model output data for building the component wherein the ICME model output data includes predicted melt pool dimensions time-series data, predicted melt temperature time-series data, and predicted defects forming as a result of melt pool evolution and movement; and estimating using the received sensor data and the received ICME model output data one or more material properties associated with the metallic component using a material property prediction module configured to predict one or more of the material properties.

A method for controlling an actuator. The method includes: mapping parameters of a trained machine learning system that have a magnitude from a first set of different possible magnitudes to a magnitude of at least one predefinable second set of different possible magnitudes; storing the converted parameters in a memory block in each case; ascertaining an output variable of the machine learning system as a function of an input variable and the stored parameters. The stored parameters are read out from the respective memory block with the aid of at least one mask. The actuated is actuated as a function of the ascertained output variable. A computer system, a computer program, and a machine-readable memory element in which the computer program is stored are also described.

The image processing device is provided with: a first input unit which, with respect to one or more virtual models including a virtual model of an operation machine, receives an input of a first parameter for identifying a type; a second input unit which receives an input of a second parameter relating to a stochastic distribution having, as a random variable, a characteristic of an element constituting the one or more virtual models; a virtual model generation unit which, using the first parameter and the second parameter, generates the one or more virtual model stochastically; a determination unit which determines the correctness of an operation of the virtual model of the operation machine when operated in a virtual space including the one or more stochastically generated virtual models; and a learning unit which learns a control module for the operation machine for achieving a predetermined operation.

Systems and methods are provided for maintaining an air conditioning system. A system can include one or more sensors positioned inside of the air conditioning system configured to transmit current sensor data to a remote location. A data repository contains historic sensor data and corresponding air conditioning system status data. A neural network is trained using the historic sensor data and the corresponding air conditioning system status data to predict a future air conditioning system status based on the transmitted current sensor data. A server computer system is configured to predict the future air conditioning system status based on the current sensor data using the neural network, and a graphical user interface is configured to display the predicted future air conditioning system status to a remote client. The current sensor data is stored in the data repository and the neural network is further trained based on the current sensor data.