In order to monitor the operational status of a plant, a plant monitoring apparatus 10 includes: a control program acquisition unit 11 configured to acquire a control program for controlling the plant on the basis of sensor data from a sensor installed in the plant; a causal relationship extraction unit 12 configured to extract, from the control program, causal relationships between a plurality of signals that are used in the plant; a causal relationship specification unit 13 configured to specify the current state of the plurality of signals and compare the specified state and the extracted causal relationships to specify a causal relationship corresponding to the specified state; and a display unit 14 configured to display the specified causal relationship on a screen.

A component security device may be disposed at an interface between a component and a cyber-physical system. The disclosed component security device may be physically and/or electrically coupled between the component and infrastructure of the cyber-physical system, such as a backplane, bus, and/or the like. The component security device may be configured to monitor the component, and selectively isolate the component from the cyber-physical system. Since the component security device is interposed at the interface of the component, the component security device may be capable of isolating the component regardless of whether the component has been compromised (e.g., regardless of whether the component is capable of complying with system commands).

The present disclosure relates to a portable production machine, a mobile terminal, a production system having a portable production machine and a mobile terminal, and a method for capturing data. According to aspects of the disclosure, on the production machine - which can be, for example, a welding machine or cutting machine - production data is automatically captured, organized into data sets, and converted into data set images, which are displayed on a display device. The displayed data set images are captured with a mobile terminal and transmitted to a central server. As a result, a plurality of production machines can be incorporated into a data system without the need for any changes to the hardware in the production machines.

Two or more computational services are defined that each represent a respective different manufacturing capability used to partially create a target part model. A common space shared among the computational services is defined to reference the target part model and manufacturing primitives corresponding to each capability. The computational services are queried to construct a logical representation of the planning space based on intersections among the primitives. One or more process plans are formed using the different manufacturing capabilities to manufacture the part.

An object of the present subject matter is to appropriately acquire and analyze a camera image for monitoring and to constantly monitor a monitoring target with high accuracy. A programmable logic controller (PLC) includes: an image processing section for sequentially acquiring the image data of the camera image from the camera sensor and generating characteristic amount data indicating a characteristic amount of image data in a preset monitoring region; a time-series data acquisition section for sequentially collecting characteristic amount data from the image processing section and acquiring time-series data of a characteristic amount; and a monitoring section for monitoring time-series data of a current characteristic amount acquired by the time-series data acquisition section in accordance with a monitoring timing defined by the device of the device memory.

A system (10) for monitoring and/or controlling one or more chemical plant(s) (12) including at least one processing layer (14, 16, 32, 34), wherein the at least one processing layer (14, 16, 32, 34) is associated with a secure network (20) and communicatively coupled to an interface (26) for providing process or asset specific data or process applications to an external processing layer (30), wherein the at least one processing layer (14, 16, 32, 34) is configured to add a transfer tag to the process or asset specific data or to the process application and to provide the process or asset specific data or the process application based on the transfer tag.

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.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

Method, apparatus and computer program product for detecting vulnerability in an industrial control system, predicting maintenance in an industrial control system, and defining authorization rules for peripheral devices based on peripheral device categorization are described herein.

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.