A method includes obtaining data associated with operation of a model-based industrial process controller. The method also includes identifying at least one estimated impact of at least one operational problem of the industrial process controller, where each estimated impact is expressed in terms of a lost opportunity associated with operation of the industrial process controller. The method further includes presenting the at least one estimated impact to a user. The at least one estimated impact could include impacts associated with noise or variance in process variables used by the industrial process controller, misconfiguration of an optimizer in the industrial process controller, one or more limits on one or more process variables, a quality of at least one model used by the industrial process controller, a quality of one or more inferred properties used by the industrial process controller, or one or more process variables being dropped from use by the industrial process controller.

A method includes obtaining data associated with operation of an industrial process controller and identifying impacts of operational problems of the industrial process controller. The method also includes generating a graphical display for a user, where the graphical display presents one or more recommended actions to reduce or eliminate at least one of the impacts of at least one of the operational problems. The method further includes triggering at least one of the one or more recommended actions based on input from the user. The method could also include executing one or more analytic algorithms to process the obtained data and identify the operational problems of the industrial process controller. Each of the one or more analytic algorithms could be instantiated as a container, and multiple containers could be instantiated and executed as needed. Results of executing the one or more analytic algorithms could be transformed into a standard format.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

A method of augmenting a reliability model of products including a particular product in use by an end user. The particular product includes a first component and a second component each having component attributes. The method also includes receiving first data of the first component and second data of the second component generated during use of the particular product by the end user and comparing the first data to the second data. The method also includes identifying a measure of compatibility between the components, determining pairing data based on the measure of compatibility, and applying a set of pairing rules to the pairing data. The method further includes modifying the reliability model based at least in part on the application of the pairing rules to the pairing data to generate an augmented reliability model and initiating one or more actions in association with the augmented reliability model.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

The present application provides a method for inspecting a display panel and an inspection apparatus. Wherein the method for inspecting the display panel including the following steps: setting substrate random sampling parameters in a manufacturing process by a manufacturing execution module, and transmitting the random sampling parameters to a production line control module; receiving and storing the random sampling parameters by the production line control module; generating a random sampling control signal and transmitting to a detector according to the random sampling parameters by the production line control module; and performing a random sampling to a substrate by the detector in accordance with the random sampling control signal.

The present application provides a method for inspecting a display panel and an inspection apparatus. Wherein the method for inspecting the display panel including the following steps: setting substrate random sampling parameters in a manufacturing process by a manufacturing execution module, and transmitting the random sampling parameters to a production line control module; receiving and storing the random sampling parameters by the production line control module; generating a random sampling control signal and transmitting to a detector according to the random sampling parameters by the production line control module; and performing a random sampling to a substrate by the detector in accordance with the random sampling control signal.

A method of augmenting a reliability model of products including a particular product in use by an end user. The particular product includes a first component and a second component each having component attributes. The method also includes receiving first data of the first component and second data of the second component generated during use of the particular product by the end user and comparing the first data to the second data. The method also includes identifying a measure of compatibility between the components, determining pairing data based on the measure of compatibility, and applying a set of pairing rules to the pairing data. The method further includes modifying the reliability model based at least in part on the application of the pairing rules to the pairing data to generate an augmented reliability model and initiating one or more actions in association with the augmented reliability model.

A method of augmenting a reliability model for a manufactured product includes receiving a product attribute identifying a product, receiving a first component attribute and a second component attribute, and receiving first manufacturing data and second manufacturing data, the first manufacturing data comprising manufacturing data associated with the first component and the second manufacturing data comprising manufacturing data associated with the second component. The method can include applying a set of compatibility rules to the first manufacturing data and the second manufacturing data, determining pairing data from the application of the set of compatibility rules to the first manufacturing data and the second manufacturing data, obtaining a reliability model of products including the product, augmenting the reliability model based on the pairing data, and performing one or more actions with the augmented reliability model.

Systems and methods of determining a difference of position between a malleable object and a target shape are described herein.