A monitoring device for detecting a diagnostic state of an industrial machine on the basis of vibrations associated with the industrial machine, includes at least one measurement transducer for detecting at least one first signal characterizing the vibration of the industrial machine in a time period and for detecting at least one second signal characterizing the vibration of the industrial machine in the same time period, wherein the at least one measurement transducer has at least one signal processor for evaluating the at least first signal as a first characteristic value and for evaluating the at least second signal as a second characteristic value and wherein the at least one first characteristic value and the at least one second characteristic value form a characteristic value tuple, further having a storage unit, in which different diagnostic states describing the state of the industrial machine are saved, wherein the diagnostic states are determined as a function of the characteristic value tuple, and a predefined scalar range is saved with predetermined different fixed scalar values, which define respectively an existing limit potential, wherein a respective limit potential characterizes a change in the diagnostic state, and wherein between adjacent limit potentials a diagnostic partition is formed and wherein different scalar values from the scalar range are assigned to the different diagnostic partitions and wherein different respective diagnostic partitions represent different diagnostic states of the industrial machine, and wherein the monitoring device has at least one interpolation function for mapping the characteristic value tuple on a common, unique, time-dependent scalar value in the predefined scalar range, so that by way of the time-dependent scalar value a time monitoring of the diagnostic state of the industrial machine is achieved.

A system for controlling an operation of a machine including a plurality of actuators assisting one or multiple tools to perform one or multiple tasks, in response to receiving an acoustic mixture of signals generated by the tool performing a task and by the plurality of actuators actuating the tool, submit the acoustic mixture of signals into a neural network trained to separate from the acoustic mixture a signal generated by the tool performing the task from signals generated by the actuators actuating the tool to extract the signal generated by the tool performing the task from the acoustic mixture of signals, analyze the extracted signal to produce a state of performance of the task, and execute a control action selected according to the state of performance of the task.

An operation parameter adjusting method according to an aspect includes a detecting step for causing a robot to execute a plurality of adjustment operations using candidate values of operation parameters and acquiring detection values of a detecting section, an operation parameter updating step for executing optimization processing for the operation parameters using the acquired detection values to thereby obtain new candidate values of the operation parameters, a repeating step for repeating the operation parameter updating step and the detecting step, and an operation parameter determining step for determining, based on one or more candidate values of the operation parameters obtained by the repeating step, the operation parameter used in the robot system. The detecting step includes a suspension determining step for performing continuation or suspension of the detecting step based on a result of comparison of the acquired detection values of the part of the adjustment operations and a reference value.

In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

A malfunction determination method for a production machine including a motor as a driving source of a rotating mechanism acquires sensor data of a sensor for detecting a condition of the production machine, determines whether the production machine has an operation stop period during which the production machine has stopped its operation for a predetermined period of time or longer in accordance with an operation history of the production machine, sets a malfunction determination suspension period for suspending a malfunction determination of the production machine when determined to have the operation stop period, in accordance with a length of the operation stop period, and determines whether the production machine has a malfunction in a period other than the malfunction determination suspension period.

A tool management system of a machine tool includes: a detection unit to detect at least one of vibration, acoustic waves produced during operation of the machine tool, and current value of a server motor of a driving 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.

A determination apparatus includes circuitry to receive a detection result of a time-varying physical quantity generated by rotation of a rotator attached to a rotation shaft, and rotation angle information of the rotator; and determine a rotation state of the rotator based on the detection result and the rotation angle information.

A method and robot controller configured to obtain an inertia-vibration model of the robot arm. The inertia-vibration model defines a relationship between the inertia of the robot arm and the vibrational properties of said robot arm and have been by setting the robot arm in a plurality of different physical configurations and for each of said physical configurations of said robot arm obtaining the vibrational properties and the inertia the robot arm. The inertia-vibration model makes it possible to in a simple and efficient way to obtain the vibrational properties of different physical configurations of the robot arm whereby the robot arm can be controlled according to the vibrational properties of the robot arm. This makes it possible to reduce the vibrations of the robot arm during movement of the robot arm.

A vibration analysis apparatus includes: a storage unit storing a regression equation indicating correspondence between rotation speed change of a rotation mechanism and a peak occurrence frequency of acceleration of vibration, for each acceleration peak; an analysis unit extracting a peak occurrence frequency of acceleration of vibration for each acceleration peak, based on vibration data of the rotation mechanism and calculating, for each acceleration peak, a waveform area of the acceleration peak by integrating the acceleration peak over a specific frequency section; and an anomaly determination unit determining whether operational anomaly occurs in the rotation mechanism for each acceleration peak. The analysis unit tracks, in accordance with the regression equation, change in peak occurrence frequency due to rotation speed change when vibration of the rotation mechanism is analyzed, and calculates, for each second acceleration peak, the waveform area of the second acceleration peak corresponding to the second vibration frequency tracked.

Provided is a control system that can control the operation of a robot with high accuracy. A control system 1 is provided with a sensor 44 that detects an acceleration that is based on the vibration of a robot 3, an interpolation unit 222 that interpolates a plurality of pieces of sensor data detected by the sensor 44, and a data generation unit 223 that generates combined data having a short sampling period on the basis of a plurality of pieces of interpolation data obtained through interpolation by the interpolation unit 222.