Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a priority maintenance task based on the sub-set of prior sensor logs, t and the correlated maintenance tasks; and outputting the priority maintenance task.

In an information processing device according to the present invention, a statistics estimation unit estimates a value of a state quantity by using a statistical model constructed based on values of past state quantities of a target device. A physical estimation unit estimates a value of a state quantity by using a physical model constructed based on design data of the target device. A specification unit specifies a value to be used to manage the target device from the value estimated by the statistics estimation unit and the value estimated by the physical estimation unit based on deterioration of the target device with time.

A cell control system that reduces an abnormality occurrence probability in a manufacturing cell according to a manufacturing cell operation plan includes a cell controller that transmits an operation instruction to a manufacturing machine of the manufacturing cell. The cell controller includes a machine operation instructing unit that transmits the operation instruction to each manufacturing machine based on an operation schedule, an operation information collecting unit that collects operation information of the manufacturing machine, an alarm probability estimating unit that estimates an alarm occurrence probability of the manufacturing machine, a schedule candidate creating unit that creates at least one operation schedule candidate having a changed order of an operation element of the operation schedule or start time, and an optimal candidate selecting unit that selects a shortest necessary time that takes the alarm occurrence into account from the operation schedule and the operation schedule candidate as a selected operation schedule.

A control apparatus that controls a plurality of robots, includes a failure prediction part that predicts a time of failure with respect to each component of the robots, a maintenance time adjustment part that adjusts maintenance times of the plurality of robots based on the components for which the times of failure are predicted, and a load adjustment part that adjust workloads of the robots according to the predicted times of failure for activation until the maintenance times.

Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a priority maintenance task based on the sub-set of prior sensor logs, t and the correlated maintenance tasks; and outputting the priority maintenance task.

An equipment inspection system capable of creating an inspection plan for inspecting a component part of an equipment at an appropriate timing is provided. An equipment inspection system includes an index acquisition unit, an importance level judgement unit, and an inspection time determination unit. The index acquisition unit acquires at least one influence index. The importance level judgement unit judges an importance level of an inspection on a component part based on the influence index. The inspection time determination unit determines the inspection time of the component part according to the importance level.

Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a priority maintenance task based on the sub-set of prior sensor logs, t and the correlated maintenance tasks; and outputting the priority maintenance task.

A control apparatus that controls a plurality of robots, includes a failure prediction part that predicts a time of failure with respect to each component of the robots, a maintenance time adjustment part that adjusts maintenance times of the plurality of robots based on the components for which the times of failure are predicted, and a load adjustment part that adjust workloads of the robots according to the predicted times of failure for activation until the maintenance times.