A computer-implemented method determines a property of a machine, in particular a machine tool, without metrologically capturing the property. The method includes the following steps: capturing one or more first time series of one or more physical measurement variables of the machine; detecting change points in the one or more first time series; extracting pattern-sequence instances from the first time series on the basis of the detected change points; producing a plurality of pattern-sequence classes in accordance with the extracted pattern-sequence instances; identifying at least one characteristic of a plurality of pattern-sequence instances of the same pattern-sequence class and a time curve of the characteristic; determining a property of a machine using the determined characteristic and/or using the time curve of the determined characteristic.

A system (10) for industrial automation, including a field device (1) with a function unit (2), in particular an actuator unit, sensor unit and/or control unit, which is configured to provide a function in accordance with a received payload (ND), and a communication unit (3) for receiving a payload signal (DS) containing the payload (ND) via a cable (4), the communication unit (3) being configured to provide a parameter set (PS) on the basis of the payload signal (DS) and to carry out signal processing of the payload signal (DS) using the parameter set (PS) in order to obtain the payload (ND) from the payload signal (DS), the system (10) further comprising a diagnosis device (5) which is configured to determine a cable wear condition of the cable (4) in accordance with an indicator variable (IG) based on the parameter set (PS).

A drive apparatus which controls a speed or a torque of a servo motor of a processing machine according to a command obtained by converting an external command input from outside includes a machine learning apparatus which learns appropriate conversion from the external command to the command. The machine learning apparatus includes: a state observing unit which observes the external command, the command, and a state of the processing machine or the servo motor as state variables that represent a present state of an environment; a determination data acquiring unit which acquires determination data indicating an evaluation result of processing by the processing machine; and a learning unit which learns the external command and the state of the processing machine or the servo motor, and the command, in association with each other using the state variables and the determination data.

A life prediction apparatus is configured to accurately predict a life of a cable wired at a joint part of a robot. The life prediction apparatus includes a fatigue-level estimation unit for estimating a fatigue level of the cable based on encoder information of an actuator which moves the joint part of the robot, and a life prediction unit for predicting the life of the cable based on a fatigue level of the cable estimated by the fatigue-level estimation unit and an allowable value of the cable.

A machine learning includes a state observation unit that observes, as state variables representing a current state of an environment, PID control parameter data indicating the a parameter of the PID control during machining, machining condition data indicating a machining condition of the machining, and machining environment data relating to a machining environment of the machining, a determination data acquisition unit that acquires, as determination data, tool life determination data indicating an appropriateness determination result relating to depletion of the life of a tool during the machining, and cycle time determination data indicating an appropriateness determination result relating to the cycle time of the machining, and a learning unit that learns the machining condition and the machining environment of the machining, and the parameter of the PID control in association with each other.

A member preparation method includes a request rank determining step of determining a request member rank of a member fitted to production of a mounting board based on board information and component information including a mounting position of a component mounted on the mounting board. Furthermore, the member preparation method includes a fitting member extracting step of extracting a fitting member fitted to the request member rank in a plurality of members based on a production plan of the mounting board and retained member information in which a member rank is associated with each of the plurality of the members. Furthermore, the member preparation method includes a use member selecting step of selecting a use member in the fitting member, and a member preparation instructing step of generating an instruction to prepare the use member.

A drive apparatus which controls a speed or a torque of a servo motor of a processing machine according to a command obtained by converting an external command input from outside includes a machine learning apparatus which learns appropriate conversion from the external command to the command. The machine learning apparatus includes: a state observing unit which observes the external command, the command, and a state of the processing machine or the servo motor as state variables that represent a present state of an environment; a determination data acquiring unit which acquires determination data indicating an evaluation result of processing by the processing machine; and a learning unit which learns the external command and the state of the processing machine or the servo motor, and the command, in association with each other using the state variables and the determination data.

A machine learning includes a state observation unit that observes, as state variables representing a current state of an environment, PID control parameter data indicating the a parameter of the PID control during machining, machining condition data indicating a machining condition of the machining, and machining environment data relating to a machining environment of the machining, a determination data acquisition unit that acquires, as determination data, tool life determination data indicating an appropriateness determination result relating to depletion of the life of a tool during the machining, and cycle time determination data indicating an appropriateness determination result relating to the cycle time of the machining, and a learning unit that learns the machining condition and the machining environment of the machining, and the parameter of the PID control in association with each other.

A life prediction apparatus is configured to accurately predict a life of a cable wired at a joint part of a robot. The life prediction apparatus includes a fatigue-level estimation unit for estimating a fatigue level of the cable based on encoder information of an actuator which moves the joint part of the robot, and a life prediction unit for predicting the life of the cable based on a fatigue level of the cable estimated by the fatigue-level estimation unit and an allowable value of the cable.

A member preparation method includes a request rank determining step of determining a request member rank of a member fitted to production of a mounting board based on board information and component information including a mounting position of a component mounted on the mounting board. Furthermore, the member preparation method includes a fitting member extracting step of extracting a fitting member fitted to the request member rank in a plurality of members based on a production plan of the mounting board and retained member information in which a member rank is associated with each of the plurality of the members. Furthermore, the member preparation method includes a use member selecting step of selecting a use member in the fitting member, and a member preparation instructing step of generating an instruction to prepare the use member.