Strong Force Iot Portfolio 2016, LLC

Strong Force Tx Portfolio 2018, LLC

Gerald William Duffy, Jr

Charles Howard Cella

Jeffrey P McGuckin

Mehul Desai

Tracey Ho

John Segui

Steven Jay Blumenthal

Chun Meng

Richard Spitz

Teymour El-tahry

A system includes an expert graphical user interface configured to: present a list of reliability measures of an industrial machine, facilitate a selection by a user of a reliability measure from the list of reliability measures, present a representation of a smart band data collection template associated with the reliability measure selected by the user, and a data routing and collection system configured to, in response to a user indication of acceptance of the smart band data collection template, collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.

Systems and methods for enabling user acceptance of a smart band data collection template for data collection in an industrial environment

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may perform a method of predicting a service event from vibration data captured data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine can be generated based on a severity unit calculated for the captured vibration.

Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial Internet of Things

A system can include a backend system and a sensor kit configured to monitor an industrial setting. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to the backend system via a public network. The backend system can include a processing system and a storage system, where the processing system performs backend operations on the sensor data and the storage systems stores the sensor data.

Systems for monitoring and managing industrial settings

Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing 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 for self-organizing data collection and storage in a manufacturing 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 for self-organizing data collection and storage in a power generation environment

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 in an industrial environment

Systems for self-organizing data collection and storage in a refining environment are disclosed. An example system may include a swarm of mobile data collectors structured to interpret a plurality of sensor inputs from sensors in the refining 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 a plurality of refining system components disposed in the refining environment, and wherein the plurality of refining system components is structured to contribute, in part, to refining of a product. The self-organizing system organizes a swarm of mobile data collectors to collect data from the system components, and 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.

Systems for self-organizing data collection and storage in a refining environment

Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.

Systems and methods for processing data collected in an industrial environment using neural networks

An intelligent heating system device is provided with processing, communications, and other information technology components, for remote monitoring and control and various value added features and services, embodiments of which use a renewable energy-powered electrolyzer to produce hydrogen as an on-demand fuel stream for a heating element of the heating system.

Industrial internet of things smart heating systems and methods that produce and use hydrogen fuel

An example monitoring system for data collection includes a data collector including a plurality of sensor. The system includes a data storage to store a collector route template for the plurality of sensors with a sensor collection routine defining how the plurality of sensors. The system includes a data acquisition and analysis circuit to receive detection signals and evaluate the detection values with respect to a rule, and further, based on the evaluation of the detection values with respect to the rule, to modify the sensor collection routine.

Methods and systems for the industrial internet of things

Systems and methods for monitoring a pump and a fan are disclosed. A monitoring system for a pump may include an industrial system including a pump, a data acquisition circuit to interpret a plurality of detection values corresponding to at least one of a plurality of input sensors operationally coupled to the pump and communicatively coupled to the data acquisition circuit. A monitoring system may further include a signal evaluation circuit including a timer circuit to generate at least one timing signal and a phase detection circuit structured to determine a relative phase difference between at least one of the detection values and at least one of the timing signals from the timer circuit. A response circuit may perform at least one operation in response to the relative phase difference.

Systems and methods for monitoring pumps and fans

Systems and methods for balancing remote oil and gas equipment are disclosed. An example system may include analog sensors coupled to a piece of equipment and an analog switch with a plurality of analog sensor channels, wherein a first analog sensor channel comprises a trigger channel coupled to a first of the analog sensors, and wherein a second one of the analog sensor channels comprises an input channel coupled to a second sensors. The analog switch may digitally derive a relative phase between the trigger channel and the input channel, utilize a PLL band-pass tracking filter to determine at least one of slow-speed RPMs or phase information for the piece of equipment, and a response circuit that provides a process change command to remotely balance at least one component of the piece of equipment based on the RPMs or the phase information.

Systems and methods for balancing remote oil and gas equipment

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points operationally coupled to an industrial component vibrationally coupled to a second industrial component in the industrial environment; a data storage structured to store a plurality of stored system response patterns; a data acquisition circuit; a data analysis circuit structured to: determine a measured noise pattern for the at least one industrial component; compare the measured noise pattern to the plurality of stored system response patterns to determine an identified noise pattern; and a response circuit structured to modify data collection from at least one input channel connected to data collection points operationally coupled to the second industrial component in response to the identified noise pattern.

Methods and systems for detection in an industrial internet of things data collection environment with noise detection and system response for vibrating components

A system and method for data collection and frequency analysis with self-organization functionality includes analyzing with a processor a plurality of sensor inputs, sampling with the processor data received from at least one of the plurality of sensor inputs at a first frequency, and self-organizing with the processor a selection operation of the plurality of sensor inputs.

Method for determining service event of machine from sensor data

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.

Growing community of inventors

Philadelphia, PA, United States of America

Gerald William Duffy, Jr