A method of conditioning one or more actions related to an operator controllable device is disclosed. The method includes receiving one or more action-based instructions representing one or more operator controllable device actions associated with the operator controllable device, and receiving one or more action-oriented time representations representing one or more times associated with the action-based instructions. The method also includes deriving at least one conditioned action-based instruction for a time subsequent to the times represented by the received action-oriented time representations from: the received action-based instructions, and the received action-oriented time representations. The conditioned action-based instruction, when executed, causes the operator controllable device to take one or more conditioned actions. The method also includes producing at least one signal representing the conditioned action-based instruction, which when executed, causes the operator controllable device to take the conditioned action. Apparatuses, computer-readable storage media, and systems are also disclosed.

A machine learning device, which learns shocks to a teaching device, includes a state observation unit which observes data based on an inclination of the teaching device or a present position of the teaching device; a label obtaining unit which obtains a label based on a shock received by the teaching device; and a learning unit which generates a learning model based on an output of the state observation unit and an output of the label obtaining unit.

A controller that controls a robot that performs grinding on a workpiece includes a machine learning device that learns grinding conditions for performing the grinding. The machine learning device observes, as state variables expressing a current state of an environment, a feature of a surface state of the workpiece after the grinding and the grinding conditions, acquires determination data indicating an evaluation result of the surface state of the workpiece after the grinding, and learns the feature of the surface state of the workpiece after the grinding and the grinding conditions in association with each other using the observed state variables and the acquired determination data.

A drive apparatus which controls a speed or a torque of a servo motor of a processing machine according to a command obtained by converting an external command input from outside includes a machine learning apparatus which learns appropriate conversion from the external command to the command. The machine learning apparatus includes: a state observing unit which observes the external command, the command, and a state of the processing machine or the servo motor as state variables that represent a present state of an environment; a determination data acquiring unit which acquires determination data indicating an evaluation result of processing by the processing machine; and a learning unit which learns the external command and the state of the processing machine or the servo motor, and the command, in association with each other using the state variables and the determination data.

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 failure prediction device is provided with a machine learning device configured to learn the state of a brake of a motor with respect to data on the brake. The machine learning device observes brake operating state data indicative of an operating state of the brake when the brake is in a normal state, as state variables representative of a current environmental state, and uses the observed state variables to learn a distribution of the state variables with the brake in the normal state.

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 manufacturing data processing system includes a plurality of manufacturing apparatuses, a plurality of data processing devices for processing manufacturing data associated with the plurality of manufacturing apparatuses, a plurality of communication channels for communicating the manufacturing data between the plurality of manufacturing apparatuses and the plurality of data processing devices, and a management device. The management device determines a combination of the data processing device that processes the manufacturing data associated with each of the plurality of manufacturing apparatuses and the communication channel that communicates the associated manufacturing data between each of the plurality of manufacturing apparatuses and the data processing device, based on the communication speed of the communication channel and the data processing capability of each of the plurality of data processing devices.

A numerical controller which controls a machine tool acquires tool information including a shape of a tool, a machining condition in machining, and information related to a machining result of a workpiece after machining. A machine learning device performs machine learning on tendency of the information related to a machining result with respect to the tool information and the machining condition based on the tool information and the machining condition used as input data and based on the information related to a machining result used as teacher data, so as to construct a learning model. The machine learning device determines whether or not a machining result is good by using the learning model based on the tool information and the machining condition before the machine tool machines a workpiece.

A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation from the detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.