A control system (100) and control method for controlling a process performed by production equipment (200). The control system has a first interface (191) configured to obtain, from a data provisioning module (310), production control data (311) for operating the production equipment (200) wherein the production control data (311) relates to operating instructions configured to control the production equipment (200) to automatically process a plurality of physical items (I1, I2) and their respective components (C1 to Cn). It further includes a risk evaluator module (110) configured to determine for each item a total risk value based on partial risk values associated with the respective components (C1 to Cn), and further configured to detect a change in the determined total risk values. It further includes a control unit (120) configured to: initiate, via a second interface (192), execution of the operating instructions for manufacturing a particular item (I1) having the lowest total risk value during a first operating time interval (OTV1); initiate termination of the execution of the operating instructions for processing the particular item (I1) if the change in the determined total risk values results in an alternative item (I2) having the lowest total risk value; and initiate, via the second interface (192), execution of the operating instructions for processing the alternative item (I2) during a second operating time interval (OTV2).

A control system and control method for controlling a process performed by production equipment. The control system has a first interface configured to obtain, from a data provisioning module, production control data for operating the production equipment wherein the production control data relates to operating instructions configured to control the production equipment to automatically process a plurality of physical items and their respective components. It further includes a risk evaluator module configured to determine for each item a total risk value based on partial risk values associated with the respective components, and further configured to detect a change in the determined total risk values. It further includes a control unit configured to: initiate, via a second interface, execution of the operating instructions for manufacturing a particular item having the lowest total risk value during a first operating time interval; initiate termination of the execution of the operating instructions for processing the particular item if the change in the determined total risk values results in an alternative item having the lowest total risk value; and initiate, via the second interface, execution of the operating instructions for processing the alternative item during a second operating time interval.

A machine learning (ML) based asset monitoring system that automatically determines damage mechanisms (DMs) and generates automatically updated visualizations of assets that include equipment and lines of a processing plant is disclosed. The asset monitoring system is communicatively coupled to the assets of the plant and continuously receives process parameters associated with the various processes and equipment in the plant. Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system. DMs are predicted for each of the assets using a ML model based on the process parameters and the corrosion loops. The data regarding the DMs, CLs and the process parameters are used to obtain equipment risk rankings for the assets. Multi-dimensional visualizations of the assets that display the state of the plant assets in real-time are generated.

A risk assessment device includes a diagnosis result storage unit that accumulatively stores diagnosis results of a plurality of diagnoses regarding each process device in association with installation sites of the process devices, a calculation model storage unit that stores a calculation model for calculating a probability of malfunction of a process device, a malfunction ratio calculation unit that calculates, based on diagnosis results regarding process devices that are and have been provided at a target installation site, a malfunction ratio that is a ratio of process devices that had malfunctioned by the time a reference period had elapsed from when the process devices were installed at the target installation site, a malfunction probability calculation unit that calculates, based on the calculation model, a probability of malfunction that is a probability that a process device provided at the target installation site will malfunction within the reference period, and a risk index value calculation unit that calculates a risk index value of a process device provided at the target installation site, through comparison between the calculated malfunction ratio and the calculated probability of malfunction.

A system comprising a computer-readable storage medium storing at least one program and a method for determining, tracking, and anticipating risk in a manufacturing facility are presented. In example embodiments, the method includes generating a risk data model for the manufacturing facility based on correlations between historical staffing conditions of the manufacturing facility and deviations from existing manufacturing procedures. The method further includes receiving projected operational data that includes information related to anticipated future staffing conditions of the manufacturing facility. The method further includes calculating a risk score based on the projected operational data using the risk data model. The method further includes causing presentation of a user interface that includes a display of the risk score.

A system comprising a computer-readable storage medium storing at least one program and a method for determining, tracking, and anticipating risk in a manufacturing facility are presented. In example embodiments, the method includes generating a risk data model for the manufacturing facility based on correlations between historical staffing conditions of the manufacturing facility and deviations from existing manufacturing procedures. The method further includes receiving projected operational data that includes information related to anticipated future staffing conditions of the manufacturing facility. The method further includes calculating a risk score based on the projected operational data using the risk data model. The method further includes causing presentation of a user interface that includes a display of the risk score.

A method for closed-loop real-time lifecycle risk management identifies, assesses, reviews and mitigates risks. Historically identified data stored in the databases are loaded, one or more users fill out questionnaires and various factors contributing to determination of the risks are calculated. If a risk is classified as an intolerable risk, the risk is notified to interested parties. A user may use the integrated risk management system to systematically and accurately identify a root cause of an error. The user may start from the highest level of the lifecycle of a product and assess the risk, followed by narrowing down the scope of an error by successively going down to lower production levels of the product. The steps may be processed in real time using remote devices connected to a server. The system allows different access levels to various users.

A control system (100) and control method for controlling a process performed by production equipment (200). The control system has a first interface (191) configured to obtain, from a data provisioning module (310), production control data (311) for operating the production equipment (200) wherein the production control data (311) relates to operating instructions configured to control the production equipment (200) to automatically process a plurality of physical items (I1, I2) and their respective components (C1 to Cn). It further includes a risk evaluator module (110) configured to determine for each item a total risk value based on partial risk values associated with the respective components (C1 to Cn), and further configured to detect a change in the determined total risk values. It further includes a control unit (120) configured to: initiate, via a second interface (192), execution of the operating instructions for manufacturing a particular item (I1) having the lowest total risk value during a first operating time interval (OTV1); initiate termination of the execution of the operating instructions for processing the particular item (I1) if the change in the determined total risk values results in an alternative item (I2) having the lowest total risk value; and initiate, via the second interface (192), execution of the operating instructions for processing the alternative item (I2) during a second operating time interval (OTV2).

Embodiments of this application provide an assembly verification method and an electronic device, and are applied to the field of computer technologies. The method includes: determining coordinates of a high-risk assembly position of each test point in a to-be-mounted component when the to-be-mounted component and a mounted component are assembled; determining, based on a three-dimensional tolerance dimension chain and the coordinates of a high-risk assembly position of each test point, a gap value distribution interval when the to-be-mounted component and the mounted component are assembled; and determining, based on the gap value distribution interval, whether there is an interference when the to-be-mounted component and the mounted component are assembled.

Systems and methods for predicting and displaying site safety metrics are provided. Some methods can include assigning respective risk index values to each of a plurality of devices, storing the respective risk index values in a memory device, identifying respective faults of each the plurality of devices in fault, retrieving, from the memory device, the respective risk index values of each of the plurality of devices in fault, prioritizing the respective faults of each of the plurality of devices in fault based on the respective risk index values of the plurality of devices in fault, and transmitting a notification message indicative of the prioritized respective faults.