A diagnosis system diagnoses presence or absence of an abnormality from data pieces collected in a factory. The diagnosis system includes (i) a diagnoser that diagnoses presence or absence of an abnormality by classifying, in accordance with a diagnosis model defining a plurality of groups, the collected data pieces into at least one of the plurality of groups, (ii) an extractor that extracts, from the collected data pieces, a candidate for a data piece to belong to a new group different from the plurality of groups, (iii) a reception device that provides candidate information relating to the candidate extracted by the extractor, (iv) and a learner that learns a new model including the new group. The diagnoser diagnoses presence or absence of an abnormality with the new model after the new model is learned.

A plurality of hierarchy information management devices include a first hierarchy information management unit managing first hierarchy information in which information on instruments is represented in a hierarchy structure, and each hierarchy information management device manages the first hierarchy information of a different one of the instruments. A monitoring device includes: a second hierarchy information management unit generating second hierarchy information based on a plurality of different pieces of the first hierarchy information acquired from the hierarchy information management devices, the second hierarchy information being hierarchy information in which the first hierarchy information is connected in a hierarchy structure; a display unit displaying information; and a display processing unit performing a process of switching between a plurality of different monitoring screens for display on the display unit, the different monitoring screens corresponding to different layers in the second hierarchy information and showing the operating state of the monitoring targets.

A visualization device includes a processor for carrying out a first displaying process of displaying, on a display device, a first screen indicating power consumption of a whole of an equipment group in each of periods included in a period group, a second displaying process of displaying, on the display device, a second screen indicating a cycle time of each of pieces of equipment included in the equipment group in a particular period, and a third displaying process of displaying, on the display device, a third screen indicating a cycle time of each of processes carried out with use of a particular piece of equipment in the particular period.

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).

An intelligent identification and warning method for an uncertain object of a production line in a digital twin environment, includes: establishing a model library for uncertain physical objects from a non-production line system; adding attribute data to the uncertain physical objects from the non-production line system; importing an established model library and added attribute data for the uncertain physical objects from the non-production line system into a model library of an existing DT production line system; performing auto-detection on an uncertain physical object entering a production line system; performing auto-detection on an actual size of the uncertain physical object entering the production line system; warning a danger for an unsafe object by means of voice prompting, system alarming and information pushing; matching a corresponding three-dimensional (3D) model in the established model library for a safe object; and loading a matched 3D model to the DT production line system.

Different cores of a multicore processor are used to provide diagnostics of sophisticated hardware without full redundancy by static assignment of the cores during individual cycles of the control program and comparison of the outputs. A method of automatically generating diverse programs for execution by these cores may modify one program to compile two different instructions without functionally changing that program through the use of DeMorgan equivalents and diverse compiler optimizations.

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.

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.