Abstract: This machining system comprises: a CAM device for generating, on the basis of three-dimensional shape data, a machining program including an operation code; and a numerical control device for controlling an electric motor of a machine tool. The machining system is provided with a monitoring device for detecting an abnormality of the machine tool on the basis of a drive state of the electric motor. The machining system is provided with a revision device for generating a revision command for revising a parameter for when the CAM device generates the machining program. The revision device transmits the revision command to the CAM device so as to revise the curvature of a tool path and/or the feed rate of a tool for when the abnormality occurred.

Abstract: An anomaly classification device determines whether a detected anomaly is a known anomaly or an unknown anomaly and presents to the user how to manage the detected anomaly not only for a case of a known anomaly but also for a case of an unknown anomaly. This anomaly classification device acquires, as anomaly data, data related to a physical quantity detected when an anomaly occurred in an industrial machine, creates a model used for determining whether or not the anomaly data is anomaly data that is based on a known anomaly cause and a model used for classifying which anomaly cause the anomaly data belongs to, and uses the created models to determine whether or not the anomaly data is based on a known anomaly cause and classify which anomaly cause the anomaly data is based on.

Abstract: This estimation device comprises a calculation unit that calculates variables indicating the drive state of an electric motor of a machine tool on the basis of a simulation program. The simulation program is a program in which a first auxiliary variable relating to a load factor for estimating the drive state of the electric motor is added to a command statement of a machining program for driving the machine tool. The calculation unit calculates changes in the speed of the electric motor over time on the basis of output characteristics of the electric motor, inertia related to driving of the electric motor, and the first auxiliary variable.

Abstract: This abnormality detection device: causes a motor that drives a drive unit at a plurality of rotational speeds to perform a detection operation based on a command for diagnosing the state of the drive unit, acquires, as information indicating the operating state of the drive unit, a control command for the motor driving the drive unit or a feedback signal from the motor driving the drive unit, during the detection operation; analyzes the control instruction for the motor driving the drive unit or the feedback signal from the motor; determines the state of the drive unit based on the result of the analysis, and notifies, based on the determination result, that the state of the drive unit is different from that in normal time.

Abstract: A diagnosis device stores a model used for diagnosing the condition of an industrial machine in a storage unit, acquires data related to the condition of the industrial machine, and based on the acquired data, determines the condition of the industrial machine by using the model stored in the storage unit. Then, in response to detecting that a component of the industrial machine has been replaced based on the acquired data and the data related to the determined condition of the industrial machine, the diagnosis device adapts the model stored in the storage unit to the condition of the industrial machine whose component has been replaced.

Abstract: A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

Abstract: To provide an abnormality monitoring device capable of reducing the size of a mounted device, enabling high-speed response, and achieving high accuracy in detecting abnormality. The abnormality monitoring device includes: at least one filter that extracts state information pertaining to drive of a mechanism of an industrial machine using a motor, as a signal of at least one frequency band; an integrator that integrates an output of the filter in each frequency band; and an abnormality detection unit that detects abnormality based on an integrated value integrated by the integrator. The state information may be control information of the controller that controls the motor or detection information from at least one detector attached to the mechanism.

Abstract: A numerical control system detects a state amount indicating a state of an injection operation of an injection molding machine, generates a characteristic amount that characterizes the state of the injection operation from the state amount, and infers an evaluation value of the state of the injection operation from the characteristic amount. The numerical control system detects an abnormal state on the basis of the evaluation value, generates or 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 injection operation.

Abstract: A thermal displacement compensation system detects a state quantity indicating a state of a machine, infers a thermal displacement compensation amount of the machine from the detected state quantity, and performs a thermal displacement compensation of the machine based on the inferred thermal displacement compensation amount of the machine. The thermal displacement compensation system generates a learning model by machine learning that uses a feature quantity, and stores the generated learning model in association with a combination of specified conditions of individual difference of the machine.

Abstract: The data management device capable of calculating an evaluation index based on the sensor data includes: a sensor data acquisition unit that acquires the sensor data from one or more sensors installed in an industrial machine; an evaluation index calculation unit that calculates the evaluation index with use of the sensor data; a sensor data storage unit that saves the sensor data; and a sensor data deletion unit that deletes the sensor data when a total volume of the sensor data in the sensor data storage unit exceeds an upper limit. The sensor data deletion unit determines saving priority of the sensor data based on a degree of change in the evaluation index and deletes the sensor data in accordance with the saving priority.

Abstract: A controller of a motor that drives a driven body includes: an inertia estimating unit that estimates inertia on the basis of feedback information (torque and current) of the motor; a computing unit that computes an acceleration or deceleration time constant of the motor from the estimation inertia estimated by the inertia estimating unit; a storage unit that stores an inertia difference which is a difference between the estimation inertia and at least one known actual inertia and a time constant difference which is a difference between an actual acceleration or deceleration time constant corresponding to the actual inertia and an acceleration or deceleration time constant calculated on the basis of the estimation inertia; and a correction unit that corrects the acceleration or deceleration time constant calculated by the computing unit using the inertia difference and the time constant difference stored in the storage unit.

Abstract: A numerical control system detects a state amount indicating an operation state of a machine tool, creates a characteristic amount that characterizes the state of a machining operation from the detected state amount, infers an evaluation value of the operation state of the machine tool from the characteristic amount, and detects an abnormality in the operation state of the machine tool 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.

Abstract: 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.

Abstract: A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

Abstract: A controller of a motor includes: an acceleration/deceleration time constant storing unit that stores an acceleration/deceleration time constant; a position command creating unit that creates a position command value based on the acceleration/deceleration time constant; a position detection unit that detects a rotation position of the motor; a speed command creating unit that creates a speed command for the motor on the basis of the position command value and a position detection value detected by the position detection unit; an ideal response computing unit that computes an ideal response from the position command value; and a response comparing unit that compares the ideal response with an actual response detected by the position detection unit. The response comparing unit changes the acceleration/deceleration time constant stored in the acceleration/deceleration time constant storing unit when it is determined that the ideal response does not match the actual response.

Abstract: To provide a transmission control system that can efficiently utilize communication bandwidth upon transmitting measurement data of machines. A transmission control system includes: a numerical control unit that controls operating of machines and acquires operating information; an A/C conversion unit that acquires behavior information measured by way of a sensor provided to the machine; a priority determination unit that determines priority of transmission in accordance with a condition set in advance, based on types and combinations of the operating information and the behavior information; and a bandwidth control unit that controls allocation of communication bandwidth for transmitting the operating information and the behavior information, based on a usage condition of communication bandwidth and the priority.

Abstract: A motor control device capable of promptly positioning a driven object at a target position, while suppressing backlash. The motor control device includes an operation command generation section for generating a first operation command and a second operation command for first and second motors to move the driven object; a preload command generation section for generating a first preload command which is added to the first operation command and a second preload command which is added to the second operation command in order that drive axes of the first motor and the second motor provide the driven object forces in opposite directions; and a preload command adjustment section for adjusting the first preload command and the second preload command in order that an absolute value of the above forces is decreased when an operation amount exceeds a predetermined threshold value.

Abstract: A numerical control system includes: a context acquisition unit that acquires a context in a machining operation of a machine tool; a state amount detection unit that determines a control state amount of each axis of the machine tool; a state data extraction unit that extracts state data from the state amount by using an extraction pattern based on the context; a feature amount creation unit that creates a feature amount featuring the operation state of the machine tool from the state data; an inference calculation unit that calculates an evaluation value of the operation state based on the feature amount; and an anomaly determination unit that determines the operation state based on a calculation. The numerical control system can detect an anomaly of the operation state in a wider range even when a motor operation pattern during machining, a tool, a work is different.

Abstract: A controller is provided with a repeated control unit configured to calculate a position compensation value, based on a position command for each control period of a motor, a position deviation, which is the difference between the position command and a position of the motor, and a parameter for repeated control, and a machine learning device configured to predict the position compensation value calculated by the repeated control unit, and the machine learning device constructs a learning model so as to minimize an objective function based on the position command, the position deviation, and the position compensation value.

Abstract: A controller can switch a control system of a motor to a diagnostic operation mode in addition to a normal operation mode. Abnormality of the motor can be detected by taking advantage of the appearance of a clear difference between a feedback value detected when the motor is normal and a feedback value detected when the motor is abnormal if the control system is switched to the diagnostic mode.