Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

A flexible monitoring system and corresponding methods of use are provided. The system can include a base containing backplane, and one or more monitoring circuits. The monitoring circuits can be designed with a common architecture that is programmable to perform different predetermined functions. As a result, monitoring circuits can be shared between different implementations of the flexible monitoring system. Multiple bases that can be communicatively coupled in a manner that establishes a common backplane between respective bases that is formed from the individual backplanes of each base. Each monitoring circuit is not limited to sending data to and/or receiving data from the backplane to which it is physically coupled but can instead can communicate along the common backplane. Computational processing capacity can be increased or decreased independently of input signals received by addition or removal of processing circuits from the monitoring system.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

Described in detail herein is an automated fulfilment system including a computing system programmed to receive requests from disparate sources for physical objects disposed at one or more locations in a facility. The computing system can combine the requests, and group the physical objects in the requests based on object types or expected object locations. Autonomous robot devices can receive instructions from the computing system to retrieve a group of the physical objects and deposit the physical objects in storage containers.

A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

Provided are a data acquisition system and method. The system includes: data acquisition units, to acquire a plurality of pieces of data related to a target object within a data acquisition period; and a controller, communicatively connected to the data acquisition units, to set, according to an ith data acquisition period, a sampling interval time of the ith piece of data, and to set a polling interval time according to a minimum data acquisition period of the data acquisition units. Upon the controller starting to perform polling on the data acquisition units through n_1 polling interval times until a condition is satisfied for the first time, the controller performs first polling, for the ith piece of data, on a data acquisition unit of the data acquisition units for acquiring the ith piece of data.

A method and apparatus for detecting an abnormality of a manufacturing facility is disclosed. According to an example embodiment of the present disclosure, a learning model generating method for manufacturing facility abnormality detection may include receiving a measured value for a normal state of a manufacturing facility collected through a multi-sensor on a time-by-time basis, generating a learning model including a predetermined weight set and training the learning model using the measured value, and determining, using the learning model, a threshold corresponding to a boundary between the normal state and an abnormal state of the manufacturing facility and a criterion for determining the abnormal state in a local window representing a predetermined time interval.

An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.

Disclosed is a method for monitoring and/or controlling a chemical plant (12) with multiple assets via a distributed computing system (10) with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise at least two of a first processing layer (14), a second processing layer (16, 32, 34) and an external processing layer (30), the method comprising the steps of: providing (60) a containerized application (48, 50) including an asset or plant template specifying input data, output data and an asset or plant model, deploying (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the chemical plant (12), providing (66) the generated output data for controlling and/or monitoring the chemical plant (12).

Techniques to facilitate feature licensing of an industrial controller employed in an industrial automation environment are disclosed. In one implementation, a first private key unique to an industrial controller and a security certificate is stored in a hardware root of trust within the controller. The security certificate is signed by a certificate authority for authenticating the controller. After being authenticated, the industrial controller receives a device information package provided by the certificate authority. The device information package is encrypted with a first public key paired with the first private key and signed using a second private key assigned to the certificate authority. The controller validates the device information package using a second public key paired with the second private key and decrypts the package using the first private key. One or more functions of the industrial controller are enabled based on a license included in the device information package.