JPWO2025041238A5

JPWO2025041238A5 -

Info

Publication number: JPWO2025041238A5
Application number: JP2025541194A
Authority: JP
Filing date: 2023-08-21
Publication date: 2026-05-21

Claims

A feature quantity acquisition unit acquires the context of the production operation of an industrial machine and state quantities related to the production operation state of the industrial machine, and acquires feature quantities related to the production operation state of the industrial machine based on the acquired context and state quantities.
A feature model manipulation unit that generates, updates, deletes, and adds feature models based on the aforementioned context and the aforementioned features,
A feature model storage unit that stores the feature model generated by the feature model operation unit,
An inference calculation unit that calculates an evaluation value of the operating state of the industrial machine based on the aforementioned feature quantities and the aforementioned feature model,
A variation pattern management unit that stores and manages predetermined variation patterns of the evaluation value,
A variation pattern classification unit classifies the variation patterns of the evaluation value of the operating state of the industrial machine based on the calculation results of the inference calculation unit and the predetermined variation patterns stored in the variation pattern management unit,
A feature anomaly determination unit that determines whether the feature quantities of the industrial machine are abnormal based on the classification results of the variation pattern classification unit,
An anomaly detection device equipped with the following features.

The anomaly detection device according to claim 1, wherein the fluctuation pattern classification unit classifies the evaluation value calculated by the inference calculation unit into fluctuation patterns that continue to rise, fluctuation patterns that rise and then stabilize at a high level, fluctuation patterns that fluctuate up and down, and fluctuation patterns that rise and then return to their original value.

The machine inspection result holding unit further includes a unit that receives timely input of inspection results for the aforementioned industrial machine.
An anomaly detection device according to claim 1 or 2, wherein the feature model operation unit updates the feature model stored in the feature model storage unit based on the determination result of the feature quantity anomaly determination unit and the inspection result of the machine inspection result holding unit.

The aforementioned fluctuation pattern management unit is partially or entirely shared with other anomaly detection devices.
The abnormality detection device according to claim 1 or 2 , wherein the fluctuation pattern detected by the abnormality detection device is used in the other abnormality detection device.

A control device for controlling a motor that drives the mechanism of an industrial machine,
A control device comprising the abnormality detection device according to claim 1 or claim 2 .

An anomaly detection method that operates a computer as an anomaly detection device,
A feature acquisition step involves acquiring the context of the production operation of an industrial machine and state quantities related to the production operation state of the industrial machine, and acquiring feature quantities related to the production operation state of the industrial machine based on the acquired context and state quantities.
A feature model manipulation process that generates, updates, deletes, and adds to a feature model based on the aforementioned context and the aforementioned features,
A feature model storage step for storing the feature model generated in the feature model manipulation step,
An inference calculation step for calculating an evaluation value of the operating state of the industrial machine based on the aforementioned feature quantities and the aforementioned feature model,
A variation pattern management step for storing and managing a predetermined variation pattern of the evaluation value,
A variation pattern classification step classifies the variation patterns of the evaluation value of the operating state of the industrial machine based on the calculation results of the inference calculation step and the predetermined variation patterns stored in the variation pattern management step,
A feature anomaly determination step that determines whether the feature quantities of the industrial machine are abnormal or not based on the classification results of the variation pattern classification step,
An anomaly detection method comprising the following features.

On the computer,
A feature acquisition step involves acquiring the context of the production operation of an industrial machine and state quantities related to the production operation state of the industrial machine, and acquiring feature quantities related to the production operation state of the industrial machine based on the acquired context and state quantities.
A feature model manipulation process that generates, updates, deletes, and adds to a feature model based on the aforementioned context and the aforementioned features,
A feature model storage step for storing the feature model generated in the feature model manipulation step,
An inference calculation step for calculating an evaluation value of the operating state of the industrial machine based on the aforementioned feature quantities and the aforementioned feature model,
A variation pattern management step for storing and managing a predetermined variation pattern of the evaluation value,
A variation pattern classification step classifies the variation patterns of the evaluation value of the operating state of the industrial machine based on the calculation results of the inference calculation step and the predetermined variation patterns stored in the variation pattern management step,
A feature anomaly determination step that determines whether the feature quantities of the industrial machine are abnormal or not based on the classification results of the variation pattern classification step,
A program that makes this possible.

The device comprises two or more abnormality detection devices according to claim 1 or claim 2 ,
The aforementioned fluctuation pattern management unit is shared in part or in whole among the aforementioned anomaly detection devices.
The fluctuation patterns detected by one anomaly detection device are used in an integrated anomaly detection system by other anomaly detection devices.