Methods, devices, and assemblies for controlling pressure in a supply grid are provided. The supply grid is suitable for supplying fluid to loads. The supply grid has first sensors for measuring the flow and/or the pressure of the fluid at first locations in the supply grid and a pump for pumping the fluid or a valve for controlling the flow of the fluid. The method includes: measuring the flow and/or pressure of the fluid at the first locations in the supply grid by the first sensors; predicting the pressure at the second location in the supply grid using a self-learning system based on the measured flows or pressures, wherein the self-learning system is trained to predict the pressure at a specified location in the supply grid; and actuating the pump or the valve at least also based on the pressure predicted by the trained system at the second location.

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.

Computer-implemented methods, systems, and apparatus, including computer-readable medium, for generating an action selection policy for causing an execution device to complete a task are described. Data representing a task that is divided into a sequence of subtasks are obtained. For a specified subtask except for a first subtask in the sequence of subtasks, a value neural network (VNN) is trained. The VNN receives inputs include reach probabilities of reaching a subtask initial state of the specified subtask, and predicts a reward of the execution device in the subtask initial state of the specified subtask. A strategy neural network (SNN) for a prior subtask that precedes the specified subtask is trained based on the VNN. The SNN receives inputs include a sequence of actions that reach a subtask state of the prior subtask, and predicts an action selection policy of the execution device in the subtask state of the prior subtask.

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.

Computer-implemented methods, systems, and apparatus, including computer-readable medium, for generating an action selection policy for causing an execution device to complete a task are described. Data representing a task that is divided into a sequence of subtasks are obtained. For a specified subtask except for a first subtask in the sequence of subtasks, a value neural network (VNN) is trained. The VNN receives inputs include reach probabilities of reaching a subtask initial state of the specified subtask, and predicts a reward of the execution device in the subtask initial state of the specified subtask. A strategy neural network (SNN) for a prior subtask that precedes the specified subtask is trained based on the VNN. The SNN receives inputs include a sequence of actions that reach a subtask state of the prior subtask, and predicts an action selection policy of the execution device in the subtask state of the prior subtask.

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 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 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, and a neural net expert system using intelligent management of 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 multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment. The local data collection system includes distributed complex programmable hardware device (CPLD) chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment. The local data collection system is configured to manage data collection bands.