The invention relates to a demolition robot (1), comprising a cable (12) intended to be connected to an electric network to power a motor (21), a pump (22) that is powered by the electric motor for generating a hydraulic flow to consumers (13), wherein the motor (21) is activable at varying thermal load values (PT), depending on the current consumer's (13) need for hydraulic power, a control unit (24) arranged to receive information about the thermal load (PT) on the motor, to determine a partial thermal damage value (SL, SM, SH) at various thermal loads (PT) on the motor. To minimize the risk of thermal damage to the motor, the control unit (24) is adapted to compare said partial thermal damage values (SL, SM, SH) with a normative partial thermal damage (A) and is adapted to limit the thermal load (PT) on the motor (21) to a maximum allowable thermal load value (PTmax), if the partial thermal damage value (SL, SM, SH) exceeds the normative partial thermal damage (A) at a predetermined value (A′).

A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

Process for regulating a compressor with motor for a refrigerating system, where the temperature of the cooling site is regulated through an on-off motor mode if the temperature in the compressor exceeds an upper temperature threshold. In addition, the temperature of the cooling site is regulated through a continuous on mode of the motor as soon as the motor has cooled to a lower temperature threshold. The controller converts a variable corresponding to the cooling requirement of the cooling site into a switch signal for a valve, which results in clocked opening and closing of the valve, or uses a frequency converter, which controls the cooling liquid flow through the compressor by regulating the voltage and the frequency of the motor in that the frequency converter converts a variable corresponding to the cooling requirement of a cooling site into a voltage and a frequency for the motor.

A diagnostic apparatus includes an acquiring unit that acquires state information indicating an operational state of a motor for driving a shaft of a machine tool; a sensor that measures a physical quantity indicating an environment of the machine tool; a storage unit which stores a normal range of the state information corresponding to a value of the physical quantity; and a judging unit that, when the state information acquired by the acquiring unit is not within the normal range of the state information corresponding to the value of the physical quantity measured by the sensor, judges that the machine tool has an abnormality.

A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

A diagnostic apparatus includes an acquiring unit that acquires state information indicating an operational state of a motor for driving a shaft of a machine tool; a sensor that measures a physical quantity indicating an environment of the machine tool; a storage unit which stores a normal range of the state information corresponding to a value of the physical quantity; and a judging unit that, when the state information acquired by the acquiring unit is not within the normal range of the state information corresponding to the value of the physical quantity measured by the sensor, judges that the machine tool has an abnormality.

The present invention provides a servo control system and a robot. The servo control system is applied to a servo, and includes a main control module including an angle information receiving terminal and a detection control terminal; and an angle collection module including a magnet and a magnetic encoding chip spaced apart from the magnet by a certain distance. The magnet is connected to a rotation output shaft of the servo. The magnetic encoding chip includes an angle information output terminal and a detection control receiving terminal. In the above manner, the present invention can accurately acquire position information of a servo.

A learning model is constructed for adjusting control information so that a cycle time becomes shorter while also avoiding the occurrence of overheating. A learning model construction device includes: an input data acquisition means that acquires, as input data, control information including a combination of an operation pattern of a spindle and parameters related to machining in a machine tool, and temperature information of the spindle prior to performing the machining based on the control information; a label acquisition means for acquiring temperature information of the spindle after having performed the machining based on the control information as a label; and a learning model construction means for constructing a learning model for temperature information of the spindle after having performed machining based on the control information, by performing supervised learning with a group of the input data and the label as training data.

A method, device, and computer program product for compensating robot movement deviations caused by a gear box as well as to a robot arrangement including such a device. The device has a drift estimating block configured to obtain motor data ({dot over (q)}.sub.r) and motor torque data () related to the motor, determine a measure of the temperature of the gear box based on the motor data ({dot over (q)}.sub.r) and motor torque data () and estimate the drift (q) based on a drift value of the robot section, the drift value in turn being obtained based on the gearbox temperature measure and a gravitational torque (.sub.grav) of the motor, and a drift adjusting block (44) configured to adjust a control value (q.sub.r) used to control the positioning of the robot based on the estimated drift (q).

A machine tool controller according to an embodiment of the present invention, for controlling a spindle and a feed axis, includes a motor temperature obtaining unit for obtaining and outputting the winding temperature of a spindle motor as a motor temperature; an inverter temperature obtaining unit for obtaining and outputting the temperature of an inverter that drives the spindle motor as an inverter temperature; a motor temperature comparator for comparing the outputted motor temperature with an overheat temperature for the motor; an inverter temperature comparator for comparing the outputted inverter temperature with an overheat temperature for the inverter; and an overheating assessment unit for imposing a restriction on the output of the spindle motor according to the smaller one of the differences between the motor temperature and the overheat temperature for the motor and between the inverter temperature and the overheat temperature for the inverter.