Abstract: The present disclosure describes systems and methods for data collection in an industrial environment. A method can include providing a plurality of sensors to components of an industrial system, interpreting the data values from the sensors in response to a sensed parameter group, the group including a fused plurality of sensors. The method may also include determining a recognized pattern value comprising a secondary value determined in response to the data values, updating the sensed parameter group in response to the recognized pattern value, and adjusting the interpreting the data values in response to the updated sensed parameter group.

Abstract: In embodiments of the present invention improved capabilities are described for a system for process monitoring through data collection in an industrial drilling environment comprising 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 industrial drilling 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; and a data analysis circuit structured to analyze the plurality of process parameter values to detect a process condition associated with the industrial drilling environment, wherein an operational process for the industrial drilling environment is altered based on the analysis of the plurality of process parameter values.

Abstract: Monitoring a processing asset for one of an oil processing facility and a gas processing facility includes a data acquisition circuit structured to interpret a plurality of detection values corresponding to input received from a detection package, the detection package comprising at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of process components; a data analysis circuit structured to analyze a subset of the plurality of detection values to determine a status parameter; and an analysis response circuit structured to adjust a process utilizing the processing asset in response to the status parameter comprising altering at least one process parameter selected from the process parameters consisting of: a temperature, an operating speed, a utilization value of one of the plurality of process components, and a process flow.

Abstract: Methods, system and apparatus for monitoring vibration sensitive industrial equipment is disclosed. The system may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of components of the equipment, a signal conditioning circuit structured to process a subset of the detection values on multiples of a key frequency, a vibration analysis circuit structured to identify vibration in at least one of the plurality of components, a data analysis circuit structured to analyze the plurality of detection values and determine a status parameter value, and an analysis response circuit structured to take an action in response to the status parameter value.

Abstract: 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.

Abstract: The present disclosure describes systems for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process. A system can include a data circuit for analyzing a plurality of sensor inputs, a network control circuit for sending and receiving information related to sensor inputs to an external system, wherein the system provides sensor data to one or more similarly configured systems, and wherein the data circuit dynamically nominates a similarly configured system capable of providing sensor data to replace the system.

Abstract: Systems and methods for detecting operating characteristics of an industrial machine are disclosed. The detecting can include generating one or more image data sets using raw data captured by one or more data capture devices and identifying one or more values corresponding to a portion of the industrial machine within a point of interest represented by the one or more image data sets. The one or more values can be compared to corresponding predicted values and a variance data set can be generated based on the comparison of the one or more values and the corresponding predicted values. An operating characteristic of the industrial machine can be identified based on the variance data and data indicating a detection of the operating characteristic can be generated.

Abstract: 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 can utilize a distributed ledger to track one or more transactions executed in an automated data marketplace for industrial Internet of Things data. The distributed ledger distributes storage for data indicative of the one or more transactions across one or more devices, wherein the data indicative of the one or more transactions corresponds to transaction records. A transaction record stored in the distributed ledger represents one or more of sensor data, the condition of an industrial machine, orders or the requests for service and parts, an issue associated with the condition of a machine, or a hash used to identify the transaction record.

Abstract: 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 detects an operating characteristic of an industrial machine, such as vibration, using one or more sensors of a mobile data collector and identify, as a condition of the industrial machine, a characteristic for the industrial machine within the knowledge base. The system can determine severity of the condition and predict and execute a maintenance action to perform against the industrial machine based on the severity of the condition.

Abstract: A system and method for causing a mobile data collector to perform a maintenance action on an industrial machine are disclosed. The mobile data collector can be deployed for detecting and monitoring vibration activity of a portion of an industrial machine. The mobile data collector can be controlled to approach a location of the industrial machine such that a vibration sensor of the mobile data collector can record a measurement of the vibration activity, which can be transmitted as vibration data to a server over a network. The server can determine a severity of the vibration activity and predict a maintenance action to perform. A signal indicative of the maintenance action can be transmitted to the mobile data collector to cause the mobile data collector to perform the maintenance action. A record of the predicted maintenance action can be stored within a ledger associated with the industrial machine.

Abstract: An industrial machine predictive maintenance system may collect data indicative of operating characteristics of an industrial machine. The system can include a computerized maintenance management system (CMMS) that produces orders and/or requests for service and parts responsive to industrial machine service recommendations, including a mobile data collector that indicates the industrial machine service recommendation or the produced orders or requests for service and parts to a worker who uses the mobile data collector. A self-organizing data collector can cause a new record to be stored in a ledger, the new record indicating at least one of the industrial machine service recommendation or the produced orders or requests for service and parts. The ledger can use a blockchain structure to track records of transactions for each of the orders and requests for service and parts, wherein each record is stored as a block in the blockchain structure.

Abstract: A system and method for causing a mobile data collector to perform a maintenance action on an industrial machine are disclosed. The mobile data collector can be deployed for detecting and monitoring vibration activity of a portion of an industrial machine. The mobile data collector can be controlled to approach a location of the industrial machine such that a vibration sensor of the mobile data collector can record a measurement of the vibration activity. The measurement of the vibration activity can be transmitted as vibration data to a server over a network, which can determine a severity of the vibration activity and predict a maintenance action to perform based on the severity of the vibration activity. A signal indicative of the maintenance action can be transmitted to the mobile data collector to cause the mobile data collector to perform the maintenance action.

Abstract: An industrial machine predictive maintenance system and method for determining a normalized severity measure of an impact of vibration of a component of an industrial machine. Vibration data can be captured from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine and a frequency, a peak amplitude and gravitational force of the captured vibration can be determined. A frequency range-specific segment of a multi-segment vibration frequency spectra that bounds the captured vibration based on the determined frequency can be determined, and a vibration severity level for the captured vibration data can be determined based on the determined segment and at least one of the peak amplitude and the gravitational force. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine based on the vibration severity level can be generated.

Abstract: 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.

Abstract: An industrial machine predictive maintenance method and system may include an industrial machine data analysis facility that collects data representative of conditions of portions of industrial machines received via a data collection network. Vibration data representative of a vibration of at least a portion of an industrial machine can be received from a wearable device including at least one vibration sensor used to capture the vibration data. A frequency of the captured vibration can be determined by processing the captured vibration data and, based on the frequency, a segment of a multi-segment vibration frequency spectra that bounds the captured vibration can be determined. A severity unit for the captured vibration can be calculated based on the determined segment a signal in a predictive maintenance circuit for executing a maintenance action on at least the portion of the industrial machine based on the severity unit can be generated.

Abstract: In 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 perform a method of sampling a signal at a streaming sample rate to produce a plurality of samples of the signal. Portions of the plurality of samples can be allocated to first and second signal analysis circuits based on signal analysis sampling rates less than the streaming sample rate, and the samples and the outputs of the signal analysis circuits can be stored. The system can include a sensor detecting a condition of an industrial machine to output a signal, which can be sampled at a streaming sample rate that is at least twice a dominant frequency of the signal.

Abstract: Methods and systems for detecting operating characteristics of an industrial machine in which the systems include at least one data capture device configured to capture raw data of a point of interest of the industrial machine and a computer vision system. The computer vision system can generate one or more image data sets using the raw data captured, identify one or more values corresponding to a portion of the industrial machine within the point of interest represented by the one or more image data sets, compare the one or more values to corresponding predicted values, generate a variance data set based on the comparison of the one or more values and the corresponding predicted values, detect an operating characteristic of the industrial machine based on the variance data, and generate data indicating the detection of the operating characteristic.

Abstract: 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 predict a service event from vibration data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine signal a predictive maintenance server to execute a corresponding maintenance action on the portion of the industrial machine.

Abstract: 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 perform a method of image capture of a portion of an industrial machine in which an image capture template is provided and aligned via augmented reality with a live image in order to update a procedure for performing a service that implements a predicted maintenance action on an industrial machine. The system may perform a method of machine learning-based part recognition in which a captured image is analyzed and used to adapt a target part template, image analysis rules, or part recognition. The system may detect operating characteristics of an industrial machine via a machine learning aspect trained based on image data sets.

Abstract: Methods and systems for noise detection and removal in a mixer/agitator are disclosed. An example monitoring system for data collection in an industrial environment may include a data collector coupled to a plurality of input channels connected to data collection points coupled to at least one of a mixer or an agitator and a data storage to store a plurality of stored system response patterns associated with noise detection during operation. The system may further include a data acquisition circuit to interpret a plurality of detection corresponding to the input channels and a data analysis circuit to remove a background noise from the detection values, analyze the collected data to determine a measured noise pattern; and compare the measured noise pattern to the stored system response patterns to determine an identified noise pattern.