The present disclosure describes system and methods for inferring energy usage at multiple levels of granularity. One embodiment describes an industrial automation system including a first industrial automation component, a first sensor coupled to the first industrial automation component, in which the first sensor measures a first amount of power supplied to the first industrial automation component, a second industrial automation component that couples to the first industrial automation component, and an industrial control system that infers energy usage by the first industrial automation component and the second industrial automation component based at least in part on the first amount of power supplied to the first industrial automation component.

A CPU box of each mounting machine module obtains MAC addresses of communication devices of both an internal device and a base by communicating with the communication devices of both the internal device and the base after the power is turned on, compares the obtained MAC address of the internal device side and the obtained MAC address of the base side, with storage data of the MAC addresses of both the internal device side and the base side read from a non-volatile storage medium of the CPU box, obtains management data of the mounting machine module stored in association with the MAC address of the internal device side from the non-volatile storage medium of a management computer in a case where the MAC address of the internal device side does not match the storage data, and obtains the management data of the mounting machine module stored in association with the MAC address of the base side from the non-volatile storage medium of the management computer in a case where the MAC address of the base side does not match the storage data.

Embodiments are directed towards providing analytics and marketplace data to members of a user community. In some embodiments, the analytics and marketplace data are generated based on machine data provided by the members. The analytics and marketplace data may enable automatically identifying, configuring, monitoring, controlling, managing, and/or maintaining a machine or a collection/system of machine components. The analytics may include, but are not limited to analytics related to machine component reliability, machine maintenance conditions, machine prohibited conditions, machine usages, machine alert conditions, and the like. The marketplace data may include information relating to the maintenance of the machine, replacement components or alternative components for the machine, and the like for various machines. Marketplace data may include an aggregation of electronic (e)-commerce data. The marketplace data may be provided is based on data aggregated from various sources, including vendors, suppliers, buyers, sellers, online auctioneers, or other members of the user community.

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.

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.

A method for controlling a virtual assistant for an industrial plant includes receiving by an input interface an information request, wherein the information request comprises at least one request for receiving information about at least part of the industrial plant; determining by a control unit a model specification using the received information request; determining by a model manager a machine learning model using the model specification; and providing by the control unit a response to the information request using the determined machine learning model.

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).

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. An embodiment of the present invention is configured to chronologically store log data in a first storage medium, and store attribute information indicating a relevance between a type of a failure expected to occur in a facility and each of a plurality of data users; The embodiment is further configured to select, at the occurrence of a failure in the facility, a data user who has a relevance to a type of the failure from among the plurality of data users based on the attribute information, selectively read log data relating to an operation state of the facility in which the failure has occurred, and transmits the read log data to the selected data user.

A substrate processing system includes a process chamber, one or more robot, a substrate measurement system, and a computing device. The process chamber may process a substrate that will comprise a film and/or feature after the processing. The one or more robot, to move the substrate from the process chamber to a substrate measurement system. The substrate measurement system may measure the film and/or feature on the substrate and generate a profile map of the film and/or feature. The computing device may process data from the profile map using a first trained machine learning model, wherein the first trained machine learning model outputs a first chamber component condition estimation for a first chamber component of the process chamber. The computing device may then determine whether to perform maintenance on the first chamber component of the process chamber based at least in part on the first chamber component condition estimation.

A method for facilitating dismantling of a photovoltaic module includes steps of: obtaining, by an input module, product data of the photovoltaic module from a module information indicator provided on the photovoltaic module; retrieving, by a processor, a product identification code of the photovoltaic module by matching the product data against a database; generating, by an equipment controller, a control command based on the product identification code; and sending, by the equipment controller, the control command to a dismantling equipment to set equipment configuration parameters for the dismantling equipment to dismantle the photovoltaic module.