A method for directly determining a theoretical damage of at least one component of a device includes providing load-specific reference data in an evaluation unit, sensing actual load-specific data by a load sensing system, and transmitting the actual, load-specific data to the evaluation unit. The actual load-specific data includes classified load collectives comprising a dwell time of occurring damage variables at defined load levels, a number of load changes of occurring damage variables, and an event count of occurring damage variables. The method further includes scaling the load-specific reference data to the actual load-specific data for calculating the theoretical damage of the at least one component and determining a remaining service life.

A failure prediction method of predicting a failure of a component of a robot including a robot arm having the component and a detection section that detects information on vibration characteristics when the robot arm moves, includes generating a failure prediction model for prediction of the failure of the component by machine learning based on the information on vibration characteristics, and predicting the failure of the component based on an estimated value of failure prediction output by the generated failure prediction model when the information on vibration characteristics is input to the generated failure prediction model.

A method for directly determining a theoretical damage of at least one component of a device includes providing load-specific reference data in an evaluation unit, sensing actual load-specific data by a load sensing system, and transmitting the actual, load-specific data to the evaluation unit. The actual load-specific data includes classified load collectives comprising a dwell time of occurring damage variables at defined load levels, a number of load changes of occurring damage variables, and an event count of occurring damage variables. The method further includes scaling the load-specific reference data to the actual load-specific data for calculating the theoretical damage of the at least one component and determining a remaining service life.

A robot including a robot mechanism including joints and drive units, a control unit controlling the drive units so that an inspection operation to inspect one target drive unit among the drive units is executed by the robot mechanism, and a notification unit notifying maintenance information of the target drive unit based on a current value of a motor of the target drive unit during the inspection operation, or on information associated with the current value, and the inspection operation includes transmitting, to the motor of the target drive unit, control command to rotate a joint as much as a predetermined rotation angle, and thereby moving a tip of the robot mechanism or a tool at the tip, close to an object at a predetermined position from a predetermined start position, to press the object, and separating the tip of the robot mechanism or the tool away from the object.

A deterioration degree of a robot body is precisely evaluated. A robot control device 300 includes: a drive control unit 309 controlling operation of a robot body 200; a detection unit 310 detecting a signal used for analysis of a feature amount quantitatively indicating a deterioration degree of the robot body 200 deteriorated over time as the robot body 200 is operated; a determination unit 304 determining whether a data section of the signal includes a constant speed section equal to or greater than a given section; a normalization unit 305 normalizing a signal in a non-constant speed section when the data section of the signal does not include the constant speed section equal to or greater than the given section; an analysis unit 307 analyzing the feature amount; and an estimation unit 308 estimating a remaining life of the robot body 200 based on the feature amount.

A remaining life of a robot body is precisely estimated. A robot control apparatus 300 includes: a drive control unit 305 that controls drive of a robot body 200; a detection unit 306 that detects a feature amount quantitatively indicating a deterioration degree of the robot body 200 that is deteriorated over time as the robot body 200 is driven; a determination unit 303 that determinates presence/absence of a sign of malfunction of the robot body 200 based on the feature amount; and an estimation unit 304 that estimates a remaining life of the robot body 200 when presence of a sign of malfunction of the robot body 200 is determined.

A control unit causes a sensor to start sampling at a plurality of start timings different from each other. The control unit combines sampling signals obtained by the start of the sampling at the different start timings, and detects a malfunction of an apparatus in accordance with the combined sampling signal.

A tool management system of a machine tool capable of determining an appropriate time to replace a tool used by a machine tool is provided. The tool management system includes: at least one detection unit among: a vibration detection unit attached to a spindle that supports a tool of a machine tool to detect vibration; a sound detection unit provided in the vicinity of the spindle to detect acoustic waves produced during operation of the machine tool; and a servo motor current value detection unit that detects a current value of a servo motor of a driving device of the machine tool; a tool replacement determination unit that determines the necessity to replace the tool on the basis of information related to a detection value of at least one of the vibration, the acoustic waves, and the current value detected during operation of the machine tool; and a detection start/end command setting unit that adds commands for a detection start point and a detection end point of at least one of the vibration, the acoustic waves, and the current value of the servo motor to a machining program.

Provided is a tool life estimating device that enables estimation of a life of a tool used in a machine tool according to changes in machining conditions. The tool life estimating device includes a state observation unit that acquires machining information indicative of a status of the machining in a state where the life of the tool remains sufficiently, wherein the machining information is acquired from log data recorded while the machine tool is operated, and creates input data based on the machining information that has been acquired; a learning unit that constructs a learning model in which clusters of the machining information are created by unsupervised learning using the input data that has been created by the state observation unit; and a learning model storage unit that stores the learning model.

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.