Embodiments are directed towards automatically identifying, configuring, monitoring, controlling, managing, and maintaining a machine, via collection computers in communication with the machine components. The components include ID Tags that store identification data, such as a component type and a unique identifier. Interrogation of the ID Tags enables the automatic identification and configuration of the machine. Data provided by the sensors, during usage of the machine, enables the remote monitoring and managing of the usage, as well as maintaining of the machine. Machine maintenance includes automatically predicting and scheduling the replacement of various components. Embodiments provide suggestions for suppliers of replacement components, as well as suggestions for alternative components that may be better optimized for the configuration and usage of the machine. Heuristics and crowd-generated data, via machine user social networks, inform predictive analyses employed to automatically identify, configure, manage, operate, and maintain the machine.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that can adjust operations of a manufacturing plant based on an assessment of risk to the plant's operations posed by the conditions and/or operations of the different devices in the manufacturing plant. Methods may include obtaining, using a set of sensors, a set of current operational characteristics for a plurality of plant devices in a manufacturing plant. For a particular plant device, a set of risk factors corresponding to a failure of the particular plant device can be analyzed. Based on the set of risk factors, an overall risk posed by the particular plant device to operations of the manufacturing plant can be determined. Based on the overall risk, one or more operations of the manufacturing plant can be adjusted.

3-D printing batch analysis is disclosed. A disclosed example apparatus includes a processor to generate a plurality of batches, wherein each of the batches represent an arrangement of a plurality of parts to be printed in a volume, discretize the batches into slices, and determine risk values of the slices based on the respective geometric primitives. The processor is to also determine aggregate risk values corresponding to the batches based on the risk values of the slices of the respective batches, and select a batch of the plurality of batches based on the aggregate risk values. The example apparatus also includes a printer to print the selected batch in the volume.

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.

3-D printing batch analysis is disclosed. A disclosed example apparatus includes a processor to generate a plurality of batches, wherein each of the batches represent an arrangement of a plurality of parts to be printed in a volume, discretize the batches into slices, and determine risk values of the slices based on the respective geometric primitives. The processor is to also determine aggregate risk values corresponding to the batches based on the risk values of the slices of the respective batches, and select a batch of the plurality of batches based on the aggregate risk values. The example apparatus also includes a printer to print the selected batch in the volume.

Disclosed are a system, method, and non-transitory computer readable medium to model risks and automatically evaluate safety and/or security compliance of a system under assessment (SUA). The disclosure is especially suited for an independent assessment of the SUA and does not form part of the SUA.

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 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.