Non-Transitory Computer Readable Recording Medium, Abnormality Detection Method, Abnormality Detection Apparatus, Molding Machine System and Method of Generating Learning Model

An abnormality detection method for detecting a presence or an absence of an abnormality of a machine having a movable part,

• the method causing a computer to execute the processing of:
• acquiring physical quantity data on a time-series basis output from a sensor detecting a physical quantity related to a motion of the movable part;
• converting the physical quantity data on a time-series basis acquired to time-series data image representing the physical quantity data;
• inputting the time-series data image converted to a learning model to calculate a feature of the time-series data image, the learning model being trained with a feature of the time-series data image related to the movable part in a normal condition;
• and
• determining a presence or an absence of an abnormality of the machine based on the feature calculated.

An abnormality detection apparatus detecting an abnormality of a machine having a movable part, comprising:

• a sensor that detects a physical quantity related to a motion of the movable part;
• an acquisition unit that acquires physical quantity data on a time-series basis output from the sensor;
• a conversion unit that converts the physical quantity data on a time-series basis acquired by the acquisition unit to a time-series data image representing the physical quantity data;
• a calculation unit that inputs the time-series data image converted to a learning model to calculate a feature of the time-series data image, the learning model being trained with a feature of the time-series data image related to the movable part in a normal condition;
• and
• a determination unit that determines a presence or an absence of an abnormality of the machine based on the feature calculated by the calculation unit.

A method of generating a learning model for detecting an abnormality of a twin-screw kneading extruder having a first screw and a second screw, the method causing a computer to execute processing of:

• based on time-series data indicating displacements in a first-axis direction and a second-axis direction that intersect a rotation center axis of the first screw of the twin-screw kneading extruder and time-series data indicating displacements in a third-axis direction and a fourth axis direction that intersect a rotation center axis of the second screw of the twin-screw kneading extruder, generating a plurality of time-series data images each including, in a single image, a first image rendering displacements in the first-axis direction and the second-axis direction that intersect the rotation center axis of the first screw and a second image rendering displacements in the third-axis direction and the fourth-axis direction that intersect the rotation center axis of the second screw, and
• based on training data sets including the plurality of time-series data images generated and a plurality of reference images having any feature, generating a learning model that outputs a feature according to a normal operation and an abnormal operation of the twin-screw kneading extruder in a case where a time-series data image including a first image and a second image rendering displacements of the rotation center axes of the first screw and the second screw is input.