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.

A smart factory monitoring system of the present disclosure includes a sensor module including a plurality of sensors mounted on various facilities of a factory to detect different physical properties and converting communication protocols of a plurality of sensor data into an integrated protocol, an integrated management module integrating and managing data measured by the sensor including the plurality of sensor modules through a pattern or trend analysis, a management server backing up and managing data on the pattern or trend analyzed by the integrated management module and providing the data according to a request of an external device, and a manager terminal connected to the integrated management module or the management server to perform monitoring at a remote location. Accordingly, it is possible more accurately to detect abnormal signals of equipment through trend analysis or pattern analysis for data measured by each sensor.

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 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.

The present invention relates generally to a Cloud real-time textile intelligent production information management monitoring and prewarning integrated system, which comprises a sensing device, a communication system, a server, a central information integration system and an end device. The sensing device is installed on textile automation equipments to sense the real-time state information of the textile automation equipments and connected to the communication system, and the communication system sends the real-time state information to the server, it is connected to the central information integration system, so that the real-time status can be monitored at any time to check whether it exceeds the set value to give a warning message which is displayed on the end device, forming a Cloud real-time textile intelligent production information management monitoring and prewarning integrated system for real-time monitoring of overall manufacturing process of mills, increasing the manufacturing efficiency and reducing losses greatly.

A petrochemical plant or refinery may include equipment such as pumps, compressors, valves, exchangers, columns, adsorbers, or the like. Some petrochemical plants or refineries may include one or more sensors configured to collect operation information of the equipment in the plant or refinery. A faulty condition of a process of the petrochemical plant may be diagnosed based on the operation of the plant equipment. A diagnostic system, which may receive operation information from the one or more sensors, may include a detection platform, an analysis platform, a visualization platform, and/or an alert platform.

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.

Provided are systems and methods for integrated control and safety systems (ICSSs) that employ intelligent personal protective equipment (PPE). In some embodiments, an industrial plant system includes an industrial safety system (ISS) for monitoring a safety status of an industrial plant and one or more intelligent PPE systems worn by personnel located in the industrial plant. Each of the intelligent PPE systems includes one or more intelligent PPE devices for sensing personal and environmental characteristics of a person wearing the intelligent PPE system, and transmits safety data corresponding to the personal and environmental characteristics sensed. The ISS collects the safety data, determines (based on the safety data) whether a safety incident has occurred and, in response to determining that a safety incident has occurred, executes a response to the safety incident.

A control device includes a setting detection unit that detects a setting operation on a value of a setting item related to control of the industrial machine and a notification unit configured to notify information related to the setting operation. The setting detection unit can detect a setting operation caused by execution of an MDI program, a setting operation caused by execution of a machining program, a setting operation caused by an external signal, and a setting operation caused by a higher-level management server. The notification unit provides a notification using a guidance message, a popup window, voice, an external signal, or information transmission via a network.

An apparatus, method, and non-transitory machine-readable medium provide for accurate automatic determination of alarm-operator action linkage for operator assessment and alarm guidance using custom graphics and control charts. The apparatus includes a memory and a processor operably connected to the memory. The processor receives process control system data regarding a field device in an industrial process control and automation system; extracts information from the process control system data; generates a tuple based on the extracted information; and performs a rectifying operation in the industrial process control and automation system based on the generated tuple of the field device.