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.

Systems and methods provide for automatically generating a data model that includes a first data feed conforming to industry standards where only alerts for alert triggering violations are provided. The data model further comprises a second data feed that includes both the alerts from the first data feed and a plurality of synthetic alerts for any violations that occur in a data center but do not qualify as alert triggering violations. This second data feed provides a complete picture of the performance of a data center's devices and allows for accurate analytics.

An alarm rationalization system receiving and responsive to industrial process information collected from a process control system for identifying one or more alarms and executing an artificial intelligence (AI) alarm engine. The AI alarm engine builds a process/domain model based on the received industrial process information and historized alarm information to evaluate the alarms in accordance with a predefined alarm philosophy. The AI alarm engine then generates a plurality of alarm definitions based on the model to optimize the alarms. The AI alarm engine automatically populates a Master Alarm Database (MADB) with the alarm definitions. The alarms are then rationalized based on the alarm definitions stored in the MADB.

An industrial machine management system includes circuitry that collects changeable setting data of an industrial machine, determines, based on the setting data at each time point of multiple time points, whether a change has been made to the setting data, obtains change information regarding the change when the change is determined as having been made to the setting data, determines whether a predetermined event has occurred in the industrial machine, identifies, when the predetermined event is determined as having occurred, the change information such that the change information is related to the predetermined event, and outputs the change information that is identified.

An AI-based smart asset health surveillance system for a connected system is presented. The connected system includes a plurality of production and/or process lines, wherein each of the plurality of production and/or process lines includes a plurality of assets. The smart asset health surveillance system includes a memory having computer-readable instructions stored therein; and a processor configured to execute the computer-readable instructions. The processor is configured to execute the computer-readable instructions to monitor the plurality of assets and to automatically predict one or more downtime and/or anomalous events for the plurality of assets. An AI-based smart asset health surveillance method is also presented.

An approach alerting users based on a detected defect during manufacturing quality inspection based on graphical images is disclosed. The approach initiates a device inspection, wherein a model controller collects metadata about a product to be inspected and select a first model with a highest score to identify defects in the device. The approach utilizes an API to obtain results from the inspection and after determining that another model is available, initiating the second model run via an edge device performing the inspection of the device. And the algorithm awaits a response in detecting a defect during either the first model run or the second model run, providing an alert detailing the defect detected in the device.

For equipment in a production facility, information for more appropriately determining a state of the equipment is presented. The information processing device: receives information indicative of a state of equipment in a production facility (S202); carries out a process of outputting, when the received information indicative of the state of the equipment satisfies a first condition, a first alarm for prompting maintenance of the equipment (S204); carries out a process of outputting, when the information indicative of the state of the equipment satisfies a second condition, a second alarm that indicates occurrence of abnormality or a sign of abnormality in the equipment (S206); and carries out a process of outputting, in a distinguishably recognizable manner, a tendency in which the first alarm is outputted and a tendency in which the second alarm is outputted (S207).

Systems and methods provide for automatically generating a data model that includes a first data feed conforming to industry standards where only alerts for alert triggering violations are provided. The data model further comprises a second data feed that includes both the alerts from the first data feed and a plurality of synthetic alerts for any violations that occur in a data center but do not qualify as alert triggering violations. This second data feed provides a complete picture of the performance of a data center's devices and allows for accurate analytics.

An AI-based smart asset health surveillance system for a connected system is presented. The connected system includes a plurality of production and/or process lines, wherein each of the plurality of production and/or process lines includes a plurality of assets. The smart asset health surveillance system includes a memory having computer-readable instructions stored therein; and a processor configured to execute the computer-readable instructions. The processor is configured to execute the computer-readable instructions to monitor the plurality of assets and to automatically predict one or more downtime and/or anomalous events for the plurality of assets. An AI-based smart asset health surveillance method is also presented.

A factory management system and control system are provided. The factory management system includes: a machine; multiple sensors disposed corresponding to the machine and generates multiple first sensing data; a server; and a control system coupled to the machine and the server. The control system receives the first sensing data to generate multiple corresponding control commands in real time and transmits the control commands to the machine. The control system receives a user login message and receives multiple second sensing data and displays the second sensing data in a user login status. The control system receives a user control command and transmits a second control command corresponding to the user control command to the machine. When the control system determines that an abnormal condition occurs according to the second sensing data in the user login status, the control system sends a warning message.