An abnormality determining device includes: a current value detecting unit configured to detect a current value which is a value of a drive current of a motor; a variance ratio calculating unit configured to group time-series current values detected in time series by the current value detecting unit at a predetermined time interval, to calculate a variance value of the current values of each group, and to calculate a variance ratio of each group by dividing the variance value of the current values of the corresponding group by a variance value of a reference current value of the motor when a reduction gear is normal; and a determination unit configured to determine that the reduction gear is abnormal when the variance ratios calculated by the variance ratio calculating unit for all the groups are equal to or greater than a threshold value.

A method estimates a position where an abnormality has occurred. The method for estimating a position where an abnormality has occurred in a robot includes an abnormality detection step of detecting occurrence of an abnormality, and a position detection step of detecting a position where the abnormality has occurred when the occurrence of the abnormality is detected in the abnormality detection step.

A device for determining a spindle status of a spindle of a machine tool includes a detector for detecting sensor data of the spindle for a defined time window. A processing unit analyses the sensor data through artificial intelligence by calculating a defined feature of the sensor data for the defined time window and determining a spindle status from the sensor data. An output member outputs the determined spindle status.

A device for processing rotation-dependent measured values includes a data converter, a sequencing control, and an output interface. Series of measured values, which are a function of the rotation of a shaft and of which at least one is an angle value that indicates the angular position of the shaft, are conveyable to the data converter at constant time intervals of a measuring interval. The data converter is adapted to subdivide a rotation of the shaft into sectors and, using one of the angle values as a reference angle value, to allocate received measured values to a sector, and per rotation of the shaft, to ascertain for each series of measured values precisely one result value for each sector. The result values are able to be output to the output interface.

A method estimates a position where an abnormality has occurred. The method for estimating a position where an abnormality has occurred in a robot includes an abnormality detection step of detecting occurrence of an abnormality, and a position detection step of detecting a position where the abnormality has occurred when the occurrence of the abnormality is detected in the abnormality detection step.

A failure diagnosis device applicable to a mechanical device provided with a motor as a source to drive a motion axis, and configured to acquire a moving position of the motion axis and a disturbance torque value applied to the motion axis every predetermined period, and to diagnose that a failure is occurring when the disturbance torque value is larger than a failure determination threshold, includes a disturbance torque selector configured to calculate a change from a reference value of each of the acquired disturbance torque values, and to accumulate the disturbance torque values except each disturbance torque value having the change from the reference value equal to or larger than a predetermined threshold, and a failure diagnosis unit configured to diagnose a failure of the mechanical device by using the disturbance torque values accumulated by the disturbance torque selector.

A device for determining a spindle status of a spindle of a machine tool includes a detector for detecting sensor data of the spindle for a defined time window. A processing unit analyses the sensor data through artificial intelligence by calculating a defined feature of the sensor data for the defined time window and determining a spindle status from the sensor data. An output member outputs the determined spindle status.

Systems and techniques for determining sensing margins and/or diagnostic information associated with a sensor are presented. A statistics component generates statistical data based on sensor data associated with a sensing device. A margin component generates sensing margins for the sensing device based on the statistical data. An output component generates an indicator for a changing condition associated with the sensing device based on the sensing margins. In an aspect, a diagnostic component generates diagnostic data for the sensing device based on the statistical data.

A process control technique uses production data from multiple manufacturing tools and multiple inspection or metrology tools. Total measurement uncertainty (TMU) can be calculated on the production data, which can include measurements of one or more devices manufactured using the manufacturing tools. Manufacturing steps can be ranked or otherwise compared by TMU. All production modes and recipes can be continually monitored using production data.

An abnormality determining device includes: a current value detecting unit configured to detect a current value which is a value of a drive current of a motor; a variance ratio calculating unit configured to group time-series current values detected in time series by the current value detecting unit at a predetermined time interval, to calculate a variance value of the current values of each group, and to calculate a variance ratio of each group by dividing the variance value of the current values of the corresponding group by a variance value of a reference current value of the motor when a reduction gear is normal; and a determination unit configured to determine that the reduction gear is abnormal when the variance ratios calculated by the variance ratio calculating unit for all the groups are equal to or greater than a threshold value.