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 method and device for monitoring production equipment and a storage medium includes: a measurement result of production equipment to be monitored is acquired; the measurement result of the production equipment to be monitored is evaluated on at least one evaluation dimension to obtain an evaluation result on each evaluation dimension; and a production state of the production equipment to be monitored is determined based on the evaluation result on the at least one evaluation dimension. The production state includes a normal state and an abnormal state. The at least one evaluation dimension includes at least one of a process level of products, a statistical significance of products or a distribution trend of products.

A sensor unit, a control method, and a recording medium are provided for reducing data amount of failure diagnosis data while detecting a failure of a device performing work while moving more reliably. The disclosure includes an output limiting part outputting the failure diagnosis data only for a period in which an absolute value of the acceleration is equal to or less than a predetermined threshold.

A method includes receiving a command designating a desired measurement path, including desired start and end positions. The method includes searching a data memory to find a first measurement path corresponding to the command. The first measurement path includes first start and end positions. The method includes, in response to the search being successful (the first measurement path corresponds to the command), reading the first measurement path and controlling a measuring head of a coordinate measuring machine to move along the first measurement path to capture a measurement point on the measurement object. The method includes, in response to the search being unsuccessful, calculating a second measurement path, storing the second measurement path in the data memory, and moving the measuring head along the second measurement path to capture the measurement point. The method includes determining dimensional properties of the measurement object based on the measurement point.

A method includes obtaining data identifying values of one or more controlled variables associated with an industrial process controller. The method also includes identifying periods when at least one of the one or more controlled variables has been moved to an associated limit by the controller. The method further includes, for each identified period, (i) identifying a standard deviation of predicted values for the associated controlled variable and (ii) determining a control giveaway value for the associated controlled variable based on the standard deviation. The control giveaway value is associated with an offset between the associated controlled variable's average value and the associated limit. In addition, the method includes identifying variances in the one or more controlled variables using the control giveaway values and generating a graphical display identifying one or more impacts or causes for at least some of the variances.

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 calibration method for numerical controlled machine tools (1), which uses a kinematic model to generate a compensation model for the positioning error occurring with the movement of the linear (X, Y, Z) and rotation axes (B, C, A) of a machine tool (1). The calibration method measures the positions of a calibration ball (6) with a measurement sequence which includes the combined movement of the calibration ball (6) around two rotation axes (C, B or C, A), wherein the measurements around a first rotation axis (C) include at least two rotational position movements around a second axis (B or A).

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 method for using a geometrical probe (5) with a spindle (3) of a machine tool (1), wherein a probe fetch waiting state of the machine tool (1), at least one temperature parameter related to a temperature of the spindle (3) of the machine tool (1) is determined by measuring at least one temperature value for the spindle (3), and time for fetching the geometrical probe (5) is determined depending on the at least one temperature parameter.