Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

Provided is a production control system of a factory including: a plurality of machines; an air conditioner; a power computation unit for monitoring power consumption of an entire factory; a temperature information generation unit for generating information on temperature inside the factory; wherein running status and processing condition of the machines and running status of the air conditioner are controlled to produce the products dictated by the production plan by a delivery deadline; and a machine learning unit that learns a relationship of operational status including the running status and the processing condition of the machines and the running status of the air conditioner to environmental status including production completion time according to the operational status, the temperature inside the factory, and the power consumption of the factory, and outputs operational status that brings the environmental status to a desired condition in accordance with the production plan.

A mounted board manufacturing system that manufactures a mounted board, which is a board mounted with a component. The mounted board manufacturing system includes: at least one component loading device that executes a component loading operation for loading the component on a board; a rule base with which at least one machine parameter for executing the component loading operation performed by the at least one component loading device can be calculated; an operation information aggregator that aggregates, for each component data, results of processing executed by the at least one component loading device, together with operation information; and a calculation processor that selects, as actual training data, component data corresponding to an operation result that exceeds a predetermined reference, from the operation information aggregator, and estimates at least one machine parameter of a new component, using the actual training data, the rule base, and basic information of the new component.

An electronic device and associated method are used to detect a fault in a spinning mill and to estimate one or more sources of the fault, the spinning mill including a plurality of textile machines that sequentially process textile materials. With the electronic device, the method receives parameter information of one or more of the textile machines and of one or more of the textile materials. The electronic device detects faults and location of the faults by identifying parameter information of the textile materials deviating from reference information. The electronic device is used to access configuration information of the textile machines and knowledge-based information related to possible sources of faults in the spinning mill. The method incudes using the electronic device to apply parameter information, configuration information, and knowledge-based information to one or more machine-learning algorithms to estimate the sources of the faults.

Provided is an operation system including: an evaluation model generation apparatus configured to generate, by machine learning, an evaluation model configured to output an indicator indicating a result of evaluating a state in a piece of equipment with respect to an intended target based on an operation target in the piece of equipment and a state in the piece of equipment; an operation model generation apparatus configured to generate an operation model configured to output an action corresponding to the state in the piece of equipment, by reinforcement learning in which an output of the evaluation model is set as at least a part of a reward; and a control apparatus configured to apply, to a controlled object in the piece of equipment, a manipulated variable based on the action that is output by the operation model according to the state in the piece of equipment.

Provided is an apparatus including a supply unit suppling a value of a state parameter to an operation model outputting a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit acquiring a recommendation value of a control parameter output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit acquiring a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit evaluating the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through manipulation by a human.

A failure sign detection system 100 includes: a data storage unit 160 that retains data at the time of normal operation of each action, as learning data, with respect to a manufacturing operation of a manufacturing apparatus 2 composed of a plurality of actions; a sensor (acceleration sensor 6) that measures the manufacturing operation of the manufacturing apparatus 2; an action detection unit 110 that detects an action start of each action in the manufacturing operation; a divided data collection unit 180 that divides measured data, which is measured by the acceleration sensor 6, into divided data for each action and collects the divided data; and a data analysis unit 140 that analyzes an abnormality of each action on the basis of a comparison between the divided data and the learning data of each action.

Provided is a servo controller that can prevent an unnecessary cut from being generated in a workpiece during oscillation machining. A servo controller 20 includes: an oscillation command generating unit 23 that generates an oscillation command for causing a workpiece W and a tool 11 to relatively oscillate; a position deviation estimating unit 31 that estimates an estimated position deviation from a moving command for causing the workpiece W and the tool 11 to relatively move; an adder that applies the oscillation command to a position deviation based on the moving command; a subtractor that deducts the estimated position deviation from a position deviation to which the oscillation command is applied; and a learning control unit that calculates a compensation amount from a position deviation based on the moving command after deducting the estimated position deviation.

A control system for a robotic device comprising a task embedding network to receive one or more demonstrations of a task and to generate a task embedding. The task embedding comprises a representation of the task, and each demonstration comprises one or more observations of a performance of the task. The control system includes a control network to receive the task embedding from the task embedding network and to apply a policy to map a plurality of successive observations of the robotic device to respective control instructions for the robotic device. The policy applied by the control network is modulated across the plurality of successive observations of the robotic device using the task embedding from the task embedding network.

Various embodiments of the present technology provide an integrated platform that provides acceleration tools that can be used across multiple lifecycle phases of an industrial automation system to assist users in various lifecycle phases of an industrial automation system. In accordance with various embodiments, the integrated platform can take historical designs and provide various acceleration actions (e.g., initial designs, answering specific questions, expert analysis, etc.) for a current system. Various embodiments can use a common, cross-platform data file that links activity and efficiently provides needed information to a user. Some embodiments provide and manage reviews of layouts or designs by experts (e.g., individuals and expert systems) to aid in identifying needed changes to the design or system.