A lifetime estimation device for a robot including a linear-motion mechanism including a guide member and at least one slider moving along the guide member includes: a load calculation unit that calculates, at predetermined time intervals, a load acting on each slider on a basis of a program for operating the robot and geometric parameters of the robot and a load mounted on the robot; a travel-distance calculation unit that calculates travel distances of the slider at the time intervals; a lifetime calculation unit that calculates a lifetime of the linear-motion mechanism on a basis of the loads calculated by the load calculation unit and the travel distances calculated by the travel-distance calculation unit; and a display unit that displays the calculated lifetime.

Mechanical condition monitoring of robots can be used to detect unexpected failure of robots. Data taken from a robot operation is processed and compared against a health baseline. Features extracted during the monitoring stage of robot operation are aligned with features extracted during the training stage in which the health baseline is established by projecting both onto a common subspace. A classifier which can include a distance assessment such as an L2-norm is used within the common subspace to assess the condition of the robot. Excursions of the distance assessment from a criteria indicate a failure or potential failure.

A failure diagnostic device includes a torque detector that detects disturbance torques applied to joint shafts included in a multi-axis robot, a torque grouping circuit that groups the disturbance torques according to a content of an operation executed by the multi-axis robot upon detection of each disturbance torque, a torque correction circuit that obtains a corrected disturbance torque standardized between a plurality of operations with different contents based on a representative value preliminarily set for each grouped disturbance torque and the disturbance torque detected by the torque detector, and a failure diagnostic circuit that performs a failure diagnosis on the multi-axis robot by comparing the corrected disturbance torque with a threshold.

Operation information on a robot is recorded in a temporary data recorder continually only for a predetermined period. The operation information for one cycle during a normal operation of the robot is retrieved from the temporary data recorder, and is recorded in a normal-state data recorder. If a storage control section determines that a predetermined trigger condition has occurred, some pieces of the operation information recorded in the temporary data recorder are recorded in the trigger-state data recorder. An operation information display displays the operation information in a normal state and the operation information in a trigger state so that these pieces of the operation information can be compared with each other.

A system and method for adaptive diagnostics and data collection in a connected robot-cloud environment allows for the management and use of date from a robot or fleet of robots to ensure the efficient utilization thereof. The data is collected from the robots via a software agent and is transmitted to an interface that allows action from an end-user.

A robot control device includes a parameter estimating unit that causes a robot to operate under estimation conditions input by a user and that estimates a load parameter of a load attached to the robot, a torque calculating unit that calculates operation torques of respective joints of the robot caused to operate during the estimation of the load parameter, and an alert unit that issues an alert to the user when the difference between the maximum value and the minimum value of the operation torques is equal to or less than a predetermined threshold.

Mechanical condition monitoring of robots can be used to detect unexpected failure of robots. Data taken from a robot operation is processed and compared against a health baseline. Features extracted during the monitoring stage of robot operation are aligned with features extracted during the training stage in which the health baseline is established by projecting both onto a common subspace. A classifier which can include a distance assessment such as an L2-norm is used within the common subspace to assess the condition of the robot. Excursions of the distance assessment from a criteria indicate a failure or potential failure.

A failure diagnostic device for performing a failure diagnosis on a multi-axis robot includes a position detector that detects a movement position of each of joint shafts included in the multi-axis robot, a torque detector that detects a disturbance torque applied to the joint shaft, a routine-operation determination circuit that determines whether or not the multi-axis robot is executing a predefined routine operation, from the movement position detected by the position detector, a reference-value calculation circuit that calculates a disturbance-torque reference value from the disturbance torque detected during execution of the routine operation, a torque correction circuit that corrects the disturbance torque detected while the multi-axis robot executes an operation different from the routine operation by using the disturbance-torque reference value, calculated by the reference-value calculation circuit, to thereby acquire a corrected disturbance torque, and a failure diagnostic circuit that performs a failure diagnosis.

An augmented reality (AR) system for diagnosis, troubleshooting and repair of industrial robots. The disclosed diagnosis guide system communicates with a controller of an industrial robot and collects data from the robot controller, including a trouble code identifying a problem with the robot. The system then identifies an appropriate diagnosis decision tree based on the collected data, and provides an interactive step-by-step troubleshooting guide to a user on an AR-capable mobile device, including augmented reality for depicting actions to be taken during testing and component replacement. The system includes data collector, tree generator and guide generator modules, and builds the decision tree and the diagnosis guide using a stored library of diagnosis trees, decisions and diagnosis steps, along with the associated AR data.

The present invention relates to a method and device for diagnosing a defect of a collaborative robot, the method comprising the steps in which: an electronic device generates a sensing data structure for managing sensing data collected from at least one collaborative robot; the electronic device generates an operation data structure for managing operation data associated with the operation of the collaborate robot; the electronic device generates a malfunction data structure for managing malfunction data of a point in which the severity of the operation equals to or is higher than a threshold; and the electronic device stores data collected from the at least one collaborative robot in accordance with the structures. Application to other embodiments is also possible.