A technology is described for a time division duplex (TDD) repeater with network protection. The TDD repeater can comprise a first port, a second port, and one or more amplification paths coupled between the first port and the second port. The TDD repeater can comprise a signal detector configured to measure a received signal power for a downlink (DL) signal in a first set of one or more TDD DL subframes. The TDD repeater can be further configured to adjust an uplink (UL) noise power or gain of the one or more amplification paths based on the received signal power for the DL signal in the first set of the one or more TDD DL subframes.

A test device can be used with an Open Radio Access Network (O-RAN) fronthaul and to test an uplink communication channel. The test device determines an uplink communication channel for passive intermodulation distortion (PIM) testing. The test device generates and transmits control plane (C-plane) messages to request the future RBs from an O-RAN radio unit (O-RU) installed at a cell site according to a delay time period. The test device receives user plane (U-Plane) messages from the O-RU containing data for the future RBs, and determines whether PIM is detected.

A test device can be used with an Open Radio Access Network (O-RAN) fronthaul and to test an uplink communication channel. The test device determines an uplink communication channel for passive intermodulation distortion (PIM) testing. The test device generates and transmits control plane (C-plane) messages to request the future RBs from an O-RAN radio unit (O-RU) installed at a cell site according to a delay time period. The test device receives user plane (U-Plane) messages from the O-RU containing data for the future RBs, and determines whether PIM is detected.

System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.

Systems for self-organizing data collection in an industrial environment are disclosed. An example system may include a self-propelled mobile data collector for handling a plurality of sensor inputs from sensors in the industrial environment, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may include a self-organizing system for self-organizing at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system organizes a swarm of self-propelled mobile data collectors to collect data from a plurality of target systems in the industrial environment.

Systems for self-organizing data collection and storage in a power generation environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the power generation system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

Systems and methods for data collection in an industrial production system including a plurality of components are disclosed. An example system may include a sensor communication circuit structured to interpret a plurality of data values from a sensed parameter group, the sensed parameter group including a plurality of sensors including a vibration sensor and a temperature sensor, and the plurality of sensors operatively coupled to at least one of the plurality of components; a data analysis circuit structured to detect an operating condition of the industrial production system based on detecting that the data values from the vibration sensor indicate a vibration pattern that matches a stored vibration fingerprint together with detecting that the data values from the temperature sensor indicate a change in a temperature; and a response circuit structured to modify a production-related operating parameter of the industrial production system in response to the detected operating condition.

Systems, methods and apparatus for data collection in an industrial environment are disclosed. A system according to one embodiment can include a plurality of input sensors operatively coupled to a production line, the plurality of sensors communicatively coupled to a data collector having a controller, the controller including: a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data, a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn output data patterns indicative of a state of the production line, and a response circuit structured to adjust an operating parameter of a component of the production line based on one of a mismatch or a match of the output data pattern and the state of the production line.

A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

Systems and methods for process monitoring through data collection in a production environment can include a data collector communicatively coupled to a plurality of input channels, each input channel connected to a monitoring point from which data is collected, the collected data providing a plurality of process parameter values for the production environment; a data storage structured to store collected data from the plurality of input channels; a data acquisition circuit structured to interpret the plurality of process parameter values from the collected data; a data analysis circuit structured to analyze the plurality of process parameter values to detect a process condition associated with the production environment; and a response circuit structured to adjust an operational process for the production environment in response to the detected process condition.