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 data collection in an industrial environment are disclosed. A system can include a plurality of analog sensors, wherein each of the plurality of analog sensors is operationally coupled to a respective data collection point of a machine component, and generates a respective stream of detection values. A data acquisition and analysis circuit can receive the respective stream of detection values and analyze the respective stream of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition based on an analysis of the respective stream of detection values, wherein the expert system analysis circuit utilizes a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

A vibration analysis apparatus includes: a storage unit storing a regression equation indicating correspondence between rotation speed change of a rotation mechanism and a peak occurrence frequency of acceleration of vibration, for each acceleration peak; an analysis unit extracting a peak occurrence frequency of acceleration of vibration for each acceleration peak, based on vibration data of the rotation mechanism and calculating, for each acceleration peak, a waveform area of the acceleration peak by integrating the acceleration peak over a specific frequency section; and an anomaly determination unit determining whether operational anomaly occurs in the rotation mechanism for each acceleration peak. The analysis unit tracks, in accordance with the regression equation, change in peak occurrence frequency due to rotation speed change when vibration of the rotation mechanism is analyzed, and calculates, for each second acceleration peak, the waveform area of the second acceleration peak corresponding to the second vibration frequency tracked.

Systems for self-organizing collection and storage in a distribution environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the distribution environment, wherein the sensor inputs sense at least one of an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system selected from a group consisting of a power system, a conveyor system, a robotic transport system, a robotic handling system, a packing system, a cold storage system, a hot storage system, a refrigeration system, a vacuum system, a hauling system, a lifting system, an inspection system, and a suspension system. A system may further include a self-organizing system for: a storage operation of the data, a data collection operation, or a selection operation.

An apparatus, methods and systems for data collection in an industrial environment are disclosed. A monitoring system can include a data collector communicatively coupled to each one of a plurality of input channels utilizing one of a plurality of collector routes, wherein each input channel includes data corresponding to an element of a first industrial machine, and wherein each of the plurality of collector routes includes a distinct data collection routine, a data storage circuit structured to store a plurality of detection values that corresponds to the plurality of input channels, and a data marketplace circuit structured to communicate at least a portion of the detection values to a data marketplace, wherein the data marketplace circuit performs at least one of self-organizing the data marketplace and automating the data marketplace.

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, methods and apparatus for modifying a data collection trajectory for centrifuges are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of centrifuge types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a centrifuge performance parameter. A response circuit may initiate an action in response to the centrifuge performance parameter.

A machine tool includes: a display; a tool configured to machine a workpiece; a spindle configured to rotate the tool or the workpiece; a sensor configured to detect a vibration frequency of the spindle or the tool; and a processor configured to control the machine tool. The processor is configured to: detect a chatter vibration in the spindle or the tool based on the vibration frequency; based on a frequency of the chatter vibration, a rotation speed of the spindle, and the number of cutting edges of the tool, calculate an order corresponding to the number of vibrations of the tool during a period of time until a current rotation angle of a first cutting edge of the tool reaches a current rotation angle of a second cutting edge of the tool; and present order information on the display, the order information indicating a magnitude of the calculated order.

A method of estimating cumulative damage of a vibrating machine with a base and a movable part movable relative to the base including the steps of: providing a computational model which estimates mechanical stress on a portion of the vibrating machine which uses the machine mass, and weight mass and load distribution of feed material, and acceleration to determine the estimated mechanical stress; estimating weight mass and load distribution of feed material; estimating measuring acceleration; providing the mass of the movable part, estimated weight mass and load distribution on the moving part and the estimated acceleration of the moveable part to the model obtaining estimated mechanical stress of the portion of the vibrating machine; recording the estimated mechanical stress of the portion of the vibrating machine over time; and estimating cumulative damage to the portion of the vibrating machine based on two or more successive recorded mechanical stress estimations.

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.