Described herein are methods and manufacturing systems that select fasteners for pre-feeding and, in some examples, pre-feed the selected fasteners. These methods involve aggregating historical manufacturing data, comprising hole identifications and fastener grip lengths, previously selected for these hole identifications. A specific grip length and a corresponding fastener repeatability rating are then determined for each hole identification from this historical manufacturing data. For example, a specific grip length corresponds to the most frequently selected grip length for this hole identification. In some examples, the historical manufacturing data is analyzed using machine learning. The fastener repeatability rating is compared to an operating threshold, in some examples, to determine if the corresponding grip length should be selected for a particular hole location. This grip length selection is then used for pre-feeding a corresponding fastener into an automated drilling machine, thereby saving significant processing time relative to conventional processes.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.

Provided are embodiments for obtaining time series datasets, determining resolution and section layout for a multi-layer encoded data image, determining, based on the image resolution and the section layout, a section resolution, generating image section arrays for the datasets that includes scaling the time series datasets to the section resolution to generate scaled time series datasets, generating an image section array for the time series datasets that include a first pixel value at points in the image section array corresponding to a data point of the scaled time series dataset and a second pixel value at points in the image section array that do not correspond to a data point of the scaled time series dataset, and generating, based on the image section arrays, the multi-layer encoded data image where each image section array is populated into a respective section of a respective layer of the image.

A drilling machine includes an inclination-based levelling system. The drilling machine includes a frame with front and rear portions, and a plurality of support jacks including a pair of rear jacks proximate the rear portion of the frame and a front jack proximate the front portion of the frame. Each jack is extendable and retractable. The drilling machine also includes a sensor configured to monitor and transmit sensor data, the sensor data including at least one of a roll of the drilling machine and a pressure. A controller operatively associated with the sensor, is configured to calculate a volume of debris to be produced during an operation of the drilling machine, to select a machine height setting based on the calculated volume of debris, and to extend and retract the plurality of support jacks according to the selected machine height setting.

System for data collection and monitoring in an industrial environment are disclosed. A data acquisition circuit may interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors. A data storage circuit may store sensor specifications, anticipated state information and detected values for use by a signal evaluation to determine a sensor overload status, a sensor fault status or a sensor validity value of at least one sensor in response to the plurality of detection values and at least one of an anticipated state information or a sensor specification. A response circuit may perform an operation in response to one of a sensor overload status, a sensor health status, or a sensor validity status.

Systems, methods and apparatus for network sensitive data collection are disclosed. A system according to one embodiment can include a plurality of input sensors operatively coupled to a component of an industrial environment and a data collector having a controller. The controller may include: a transmission environment circuit to determine a transmission condition corresponding to transmission of a subset of output data, a network management circuit to update a sensor data transmission protocol, a data collection band circuit to determine at least one collection parameter, a machine learning data analysis circuit to receive output data and learn at least one output data pattern, and a response circuit to adjust an operating parameter of the component based on one of a mismatch or a match of the at least one output data pattern and the state of the component.

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.

Monitoring, systems and methods for data collection in an industrial environment are disclosed. A system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data, and sense a change in operation and determine an aggregate rate of data being collected from the plurality of input channels. If the aggregate rate exceeds a throughput parameter the data analysis circuit alters the data collection to reduce the amount of data collected or, based on the sensed change, modify a collector route.