A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor driving the main shaft, including a state observation unit observing a state variable including at least one of data output from a motor controller controlling the motor, data output from a detector detecting a state of the motor, and data output from a measuring device measuring a state of the one of the main shaft and the motor; a determination data obtaining unit obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor which drives the main shaft, comprising: a state observation unit which observes a state variable comprising at least one of data output from a motor controller which controls the motor, data output from a detector which detects a state of the motor, and data output from a measuring device which measures a state of the one of the main shaft and the motor; a determination data obtaining unit which obtains determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit which learns the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data. 2. The machine learning device according to claim 1, wherein the data output from the motor controller comprises a torque command value and a velocity command value for driving the motor, the detector comprises at least one of a current detection unit which detects a current of the motor and a velocity detection unit which detects a velocity of the motor, and the measuring device comprises at least one of a vibration measuring unit which measures vibration of the one of the main shaft and the motor, a sound measuring unit which measures a sound in vicinity of the one of the main shaft and the motor, and a temperature measuring unit which measures a temperature in the vicinity of the one of the main shaft and the motor. 3. The machine learning device according to claim 1, wherein the main shaft executes a predetermined operation pattern at an interval of not less than a predetermined time, and the state observation unit observes the state variable in at least one of an accelerated state of the main shaft, a constant velocity-operated state of the main shaft, and one of a decelerated state of the main shaft and a coasted state of the main shaft upon cutoff of a driving force, in the operation pattern. 4. The machine learning device according to claim 1, wherein the learning unit learns the fault prediction of the one of the main shaft and the motor in accordance with a plurality of data sets generated for the one of the main shaft and the motor. 5. The machine learning device according to claim 1, wherein the learning unit learns a normal state only during a certain period and then detects occurrence of a fault determined by the determination data obtaining unit. 6. The machine learning device according to claim 1, wherein when the determination data obtaining unit obtains determination data indicating a fault of the one of the main shaft and the motor, the learning unit updates a conditional equation used for the fault prediction of the one of the main shaft and the motor by weighting the determination data comprised in the data set in accordance with a length of time from when the determination data is obtained until a fault occurs. 7. The machine learning device according to claim 1, wherein the state observation unit obtains a current state variable of the state variable via a network. 8. A fault prediction device which comprises the machine learning device according to claim 1 and predicts a fault of the one of the main shaft and the motor, the fault prediction device further comprising: a fault information output unit which outputs fault information indicating one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault, in response to input of a current state variable of the state variable, based on a learning result obtained by the learning unit in accordance with the data set. 9. The fault prediction device according to claim 8, wherein the learning unit learns the fault prediction of the one of the main shaft and the motor again in accordance with an additional data set generated based on a combination of the current state variable and the determination data. 10. The fault prediction device according to claim 8, wherein the machine learning device is located on a cloud server. 11. The fault prediction device according to claim 8, wherein the machine learning device is built in a controller which controls the machine tool. 12. The fault prediction device according to claim 8, wherein the machine tool comprises a plurality of machine tools, and the learning result obtained by the machine learning device is shared by the plurality of machine tools. 13. A fault prediction system comprising: the fault prediction device according to claim 8; the detector; the measuring device; and a fault information notifying unit which notifies an operator of the fault information. 14. The fault prediction system according to claim 13, wherein a time at which the fault information notifying unit notifies the operator of the fault information satisfies at least one of precedence to a time defined by a first predetermined period preceding a time at which a fault occurs, and subsequence to a time defined by a second predetermined period preceding the time at which a fault occurs. 15. A machine learning method for learning fault prediction of one of a main shaft of a machine tool and a motor which drives the main shaft, the method comprising: observing a state variable comprising at least one of data output from a motor controller which controls the motor, data output from a detector which detects a state of the motor, and data output from a measuring device which measures a state of the one of the main shaft and the motor; obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.