A method for configuring an interface device connected to a control device and a field device, wherein the method includes receiving a first machine learning application having a plurality of logical components connected in a pipeline, where the first machine learning application serves to analyze a signal from the field device utilizing a first machine learning model, generating a plurality of code blocks utilizing a translator based on the plurality of logical components of the first machine learning application, connecting the plurality of code blocks in accordance with the pipeline of the first machine learning application to generate a first output from the signal from the field device, and deploying the connected code blocks on firmware of the interface device including creating a virtual port connectable to the control device, and where the virtual port serves to transmits the first output to the control device.

A device that estimates a life of a clamping mechanism clamping rotation of a rotary table includes a machine learning device. The machine learning device observes operating state data of the rotary table and operation history data of the rotary table as a state variable indicative of a current state of an environment, and acquires life data indicative of the life of the clamping mechanism as label data. In addition, the device uses the state variable that has been observed and the label data that has been acquired and learns the operating state data and the operation history data and the life data in association with each other.

A system determining whether the feed shafts are normal or abnormal, the system including: a command generation unit that moves the feed shafts in a forward direction at a predetermined speed from a lower limit value to an upper limit value of a range of feeding movement by the numerical controller; and a feed shaft abnormality determination unit that monitors a drive torque command during the movement of the feed shafts in the forward direction by the command generation unit, compares a monitoring result during the movement in the forward direction with a normal value of the drive torque command, determines that abnormality occurs when the drive torque command deviates from the normal value, and outputs the determination result.

A method for configuring an interface device connected to a control device and a field device, wherein the method includes receiving a first machine learning application having a plurality of logical components connected in a pipeline, where the first machine learning application serves to analyze a signal from the field device utilizing a first machine learning model, generating a plurality of code blocks utilizing a translator based on the plurality of logical components of the first machine learning application, connecting the plurality of code blocks in accordance with the pipeline of the first machine learning application to generate a first output from the signal from the field device, and deploying the connected code blocks on firmware of the interface device including creating a virtual port connectable to the control device, and where the virtual port serves to transmits the first output to the control device.

A main spindle monitoring device determines an abnormality of a main spindle in a machine tool. The main spindle monitoring device includes a main spindle operation monitoring unit and a feed axis operation monitoring unit. The main spindle operation monitoring unit monitors a change of an operation condition of the main spindle based on a main spindle load. The feed axis operation monitoring unit monitors a change of an operation condition of a feed axis based on a feed axis load. The abnormality of the main spindle is determined when the main spindle operation monitoring unit detects the change of the operation condition of the main spindle and the feed axis operation monitoring unit does not detect the change of the operation condition of the feed axis in a state where a constant rotation speed is commanded to the main spindle while the feed axis is driving.

A system determining whether the feed shafts are normal or abnormal, the system including: a command generation unit that moves the feed shafts in a forward direction at a predetermined speed from a lower limit value to an upper limit value of a range of feeding movement by the numerical controller; and a feed shaft abnormality determination unit that monitors a drive torque command during the movement of the feed shafts in the forward direction by the command generation unit, compares a monitoring result during the movement in the forward direction with a normal value of the drive torque command, determines that abnormality occurs when the drive torque command deviates from the normal value, and outputs the determination result.

A bearing life prediction device includes a pressure measuring unit that measures the pressure applied to a front bearing, a coolant pressure measuring unit that measures the pressure of a coolant liquid, a detecting unit that measures or predicts the rotation number and the temperature, a storage unit that stores model information and motor specification information in correlation, a specifying unit that inputs or selects the model information, and a bearing life prediction unit that predicts the life of bearings on the basis of the motor specification information including specification information of the bearings stored in the storage unit, and each information on the pressure of the coolant liquid, the pressure applied to the front bearing, the rotation number of the motor, and the temperature of the bearings.

A main spindle monitoring device determines an abnormality of a main spindle in a machine tool. The main spindle monitoring device includes a main spindle operation monitoring unit and a feed axis operation monitoring unit. The main spindle operation monitoring unit monitors a change of an operation condition of the main spindle based on a main spindle load. The feed axis operation monitoring unit monitors a change of an operation condition of a feed axis based on a feed axis load. The abnormality of the main spindle is determined when the main spindle operation monitoring unit detects the change of the operation condition of the main spindle and the feed axis operation monitoring unit does not detect the change of the operation condition of the feed axis in a state where a constant rotation speed is commanded to the main spindle while the feed axis is driving.

A device that estimates a life of a clamping mechanism clamping rotation of a rotary table includes a machine learning device. The machine learning device observes operating state data of the rotary table and operation history data of the rotary table as a state variable indicative of a current state of an environment, and acquires life data indicative of the life of the clamping mechanism as label data. In addition, the device uses the state variable that has been observed and the label data that has been acquired and learns the operating state data and the operation history data and the life data in association with each other.

The bearing life prediction device includes a pressure measuring unit that measures the pressure applied to a front bearing, a coolant pressure measuring unit that measures the pressure of a coolant liquid, a detecting unit that measures or predicts the rotation number and the temperature, a storage unit that stores model information and motor specification information in correlation, a specifying unit that inputs or selects the model information, and a bearing life prediction unit that predicts the life of the bearings on the basis of the motor specification information including the specification information of the bearings stored in the storage unit in correlation with the model information input or selected by the specifying unit, the pressure information of the coolant liquid, the pressure information applied to the front bearing, the rotation number information of the motor, and the temperature information of the bearings.