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.

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.

The present invention relates to a method performed by a system (110) operable to configure a chemical separation system (120), the method comprising: receiving system hardware configuration data, wherein the system hardware configuration data is at least indicative of fluid manipulation modules (121_1-12N_M) of the chemical separation system (120), desired functionality of the fluid manipulation modules, and a fluid network (210) comprising fluid couplers configured to fluidly couple the fluid manipulation modules (121_1-12N_M), identifying a first set of function blocks to provide the indicated fluid manipulation modules with the desired functionality, generating a second set of function blocks by purging a third set of function blocks from the first set of function blocks to eliminate dead code using the configuration data, sending the second set of function blocks to the system (120).

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.

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.

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.

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.

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.

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.

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.