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.

Generation of feedback for a part production process based on vibrational testing of parts produced by the part production process. A response characteristic may be identified from vibrational data regarding the parts that is correlated to a process variable of the part production process. The response characteristic may relate to a state of the process variable such that identification of the response characteristic may allow for generation of feedback regarding adjustment of a process control. Such response characteristic may relate to a vibrational metric regarding vibrational data and may comprise identifying a trend in data between a plurality of parts. Also presented are approaches to evaluation of parts, including batch evaluation of parts in which collective vibrational data regarding a plurality of parts belonging to a batch are analyzed. The process control aspects may be performed independently or in combination with part evaluation.

A machine learning apparatus determines a control parameter of an active vibration isolation apparatus on which an industrial machine is mounted. The industrial machine includes a movable part, a drive source that drives the movable part, and a drive source control section that controls the drive source to position the movable part at a command position. The machine learning apparatus includes: an acquiring section that acquires, as teacher data, a positional deviation, which is a difference between the command position and an actual position of the movable part; a storage section that stores a learning model that outputs the control parameter corresponding to a state quantity concerning the industrial machine; and a learning section that updates the learning model using the teacher data.

A diagnostic apparatus includes circuitry that acquires a detection result of a time-varying physical quantity generated by a machine that performs a plurality of processes; generates a determination result of the processing based on the detection result; and outputs, to the machine, batch determination information in units of a plurality of same type processes performed by the machine. The batch determination information indicates whether at least one of the plurality of same type processes is determined as abnormal. Based on the batch determination information, the machine performs an action.

There is provided a configuration that includes: at least one transfer mechanism configured to transfer a substrate and at least one processing mechanism configured to process the substrate; an earthquake detector configured to detect an earthquake; and a controller configured to control the at least one transfer mechanism and the at least one processing mechanism according to a detection result of the earthquake detector, wherein the controller is configured to be capable of performing a stopping operation of the at least one transfer mechanism according to a P wave (initial tremor wave) and an S wave (principal fluctuation wave).

A machine learning apparatus determines a control parameter of an active vibration isolation apparatus on which an industrial machine is mounted. The industrial machine includes a movable part, a drive source that drives the movable part, and a drive source control section that controls the drive source to position the movable part at a command position. The machine learning apparatus includes: an acquiring section that acquires, as teacher data, a positional deviation, which is a difference between the command position and an actual position of the movable part; a storage section that stores a learning model that outputs the control parameter corresponding to a state quantity concerning the industrial machine; and a learning section that updates the learning model using the teacher data.

A diagnostic apparatus includes an acquisition part, an FFT part, and a determination part. The acquisition part acquires vibration data outputted by a sensor. The FFT part generates an initial waveform from the vibration data acquired when the production equipment is operated without load at installation thereof. The FFT part generates a start-up waveform from the vibration data acquired when the production equipment is operated without load. The FFT part generates an operation waveform from the vibration data acquired when the production equipment is operated with load. The determination part determines whether the production equipment has performed its production normally, based on the start-up waveform and the operation waveform. The determination part corrects a threshold(s) used for determining whether the production has been performed normally, based on the initial waveform and the start-up waveform.

Generation of feedback for a part production process based on vibrational testing of parts produced by the part production process. A response characteristic may be identified from vibrational data regarding the parts that is correlated to a process variable of the part production process. The response characteristic may relate to a state of the process variable such that identification of the response characteristic may allow for generation of feedback regarding adjustment of a process control. Such response characteristic may relate to a vibrational metric regarding vibrational data and may comprise identifying a trend in data between a plurality of parts. Also presented are approaches to evaluation of parts, including batch evaluation of parts in which collective vibrational data regarding a plurality of parts belonging to a batch are analyzed. The process control aspects may be performed independently or in combination with part evaluation.

A machine tool is configured so that: a first frequency band comprising the main shaft characteristic vibration frequency and a second frequency band comprising the mechanical structure characteristic vibration frequency are set; thresholds are stored for each of the set frequency bands; vibration components are extracted for each of the frequency bands from the output of a vibration sensor; and when the vibration amplitude exceeds a threshold in either of the frequency bands, an alarm is sounded.

Generation of feedback for a part production process based on vibrational testing of parts produced by the part production process. A response characteristic may be identified from vibrational data regarding the parts that is correlated to a process variable of the part production process. The response characteristic may relate to a state of the process variable such that identification of the response characteristic may allow for generation of feedback regarding adjustment of a process control. Such response characteristic may relate to a vibrational metric regarding vibrational data and may comprise identifying a trend in data between a plurality of parts. Also presented are approaches to evaluation of parts, including batch evaluation of parts in which collective vibrational data regarding a plurality of parts belonging to a batch are analyzed. The process control aspects may be performed independently or in combination with part evaluation.