Abstract: A tool state learning device has a storage unit for storing an arbitrary image captured by an imaging device imaging a machined surface of an arbitrary workpiece cut using an arbitrary tool, and a teacher data acquisition unit for acquiring, as input data, an arbitrary image stored in the storage unit, and acquiring, as a label, the state of the tool annotated according to prescribed levels indicating the degree of wear of the tool on the basis of the arbitrary image. The tool state learning device also has a tool state learning unit for using the acquired label and input data to perform supervised learning, and generating a learned model in which a machined surface image of the machined surface of a workpiece imaged by the imaging device is inputted and the state of the tool that cut the machined surface of the workpiece is outputted.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A cell control apparatus is provided with a load calculation part which calculates an operational load including at least one of average power consumption, maximum power consumption, and a variable indicating a component lifetime of a manufacturing cell in a predetermined period. The cell control apparatus includes an operation adjustment part which performs first control for reducing at least one of a speed and an acceleration at which the manufacturing machine drives and second control for adjusting a ratio of time of successive operations in such a manner as to reduce the operational load in such an extent that the number of products manufactured in the predetermined period is not changed.

Abstract: A cell control apparatus is provided with a load calculation part which calculates an operational load including at least one of average power consumption, maximum power consumption, and a variable indicating a component lifetime of a manufacturing cell in a predetermined period. The cell control apparatus includes an operation adjustment part which performs first control for reducing at least one of a speed and an acceleration at which the manufacturing machine drives and second control for adjusting a ratio of time of successive operations in such a manner as to reduce the operational load in such an extent that the number of products manufactured in the predetermined period is not changed.

Abstract: A robot controller having a function for mitigating damage to a robot when the robot is brought to an emergency stop, and for facilitating restoration of the robot from the emergency stop. The robot controller has a controlling part which controls a motion of a robot based on a predetermined robot program; a first detecting part which detects a predetermined abnormality which does not require the robot to be immediately stopped; a stopping condition judging part which judges as to whether or not a predetermined stopping condition is satisfied when the first detecting part detects the abnormality; and a stopping process executing part which executes a stopping process of the robot when the stopping condition is satisfied, and does not execute the stopping process when the stopping condition is not satisfied.

Abstract: 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.

Abstract: A robot controller includes a first time-series data obtaining part for obtaining first data used for fault diagnosis in time series and store the first data as first time-series data, a second time-series data obtaining part for obtaining second data used for extraction of the first data which is used for the fault diagnosis in time series and store the second data as second time-series data, a time specification part for specifying extraction time of the first data used for the fault diagnosis based on the second time-series data, a data extraction part for extracting the first data corresponding to the extraction time, and a diagnosis performing part for performing the fault diagnosis of the robot based on the first data extracted by the data extraction part.

Abstract: A robot controller having a function for mitigating damage to a robot when the robot is brought to an emergency stop, and for facilitating restoration of the robot from the emergency stop. The robot controller has a controlling part which controls a motion of a robot based on a predetermined robot program; a first detecting part which detects a predetermined abnormality which does not require the robot to be immediately stopped; a stopping condition judging part which judges as to whether or not a predetermined stopping condition is satisfied when the first detecting part detects the abnormality; and a stopping process executing part which executes a stopping process of the robot when the stopping condition is satisfied, and does not execute the stopping process when the stopping condition is not satisfied.

Abstract: A robot controller and a robot control method, by which each element constituting a robot is protected. An output of a rotary encoder attached to a servomotor is read, and the motor speed is obtained by calculating the difference between a current speed and another speed in a previous speed loop. Then, a processor of the servo controller of each link of a robot arm executes a normal speed loop control in order to calculate a torque command of the motor. Next, a load torque is estimated by using the obtained torque command and the motor speed, and the estimated load torque in each speed loop is compared with a predetermined threshold. If the load torque is larger than the threshold in at least one of the axes, the robot controller judges that an abnormality has occurred in the robot, generates an alarm or warning, and then controls the robot so as to protect the element.