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.

Instruction generation for a data processing array and microcontroller includes generating a tensor-level intermediate representation from a machine learning model using kernel expressions. Statements of the tensor-level intermediate representation are partitioned into a first set of statements and a second set of statements. From the first set of statements, kernel instructions are generated based on a reconfigurable neural engine model. The kernel instructions are executable by a compute tile of a data processing array to implement compute functions of the machine learning model. From the set of second statements, microcontroller instructions are generated based on a super-graph model. The microcontroller instructions are executable by a microcontroller of the data processing array to move data into and out from the data processing array.

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.

In order to reproduce noise components of lossy recorded operating signals of a technical system, a neural network is trained to reproduce a recorded target operating signal and a statistical distribution of a stochastic component of the recorded target operating signal on the basis of a recorded input operating signal. A current input operating signal of the technical system is then supplied to the trained neural network. An output signal having a noise component modelled on the statistical distribution is generated on the basis of the supplied current input operating signal and a noise signal. The output signal is then output as the current target operating signal for controlling the technical system.

The methods and systems for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment generally include obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine and connecting a first input of a crosspoint switch of the local data collection system to a first sensor and a second input of the crosspoint switch to a second sensor in the local data collection system. The methods and systems also include switching between a condition in which a first output of the crosspoint switch alternates between delivery of at least 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 a second output of the crosspoint switch and switching off unassigned outputs of the crosspoint switch into a high-impedance state. There is also continuously monitoring of at least a third input of the crosspoint switch with an alarm having a pre-determined trigger condition when the third input is unassigned to any of multiple outputs on the crosspoint switch.

Methods for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment include obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine and connecting a first input of a crosspoint switch to a first sensor and a second input of the crosspoint switch to a second sensor. The local data collection system manages data collection bands that define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. The local data collection system includes a neural net expert system using intelligent management of the data collection bands.

An operation management system is disclosed. The operation management system may receive a video stream from a wearable device of a user that is performing an operation in a physical environment. The operation management system may process, using an operation performance model, a set of frames of the video stream that indicates a state of a performance of the operation by the user. The operation management system may determine, based on the state of the performance by the user, a next task of the operation. The operation management system may configure display data that is associated with a physical object that is associated with the next task. The display data may be associated with an indicator that identifies the physical object and/or task information associated with performing the next task. The operation management system may provide the display data to the wearable device.

A method for the prevention of failures in the operation of a motorized door. At least one sensor provides time series sensor data of at least one variable of a motorized door. The time series sensor data is used for machine learning in order to monitor, detect and/or predict anomalies in the operation of the motorized door. There is also described a monitoring system for a motorized door that is configured to carry out the method.

The system includes a crosspoint switch in a local data collection system having multiple inputs individually assigned to any of 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 and second output configured to be switchable between a condition in which the first output switches 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. Unassigned outputs can be switched off producing a high-impedance state. The local data collection system is configured to manage data collection bands that define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform.

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.