Abnormality detector of a manufacturing machine using machine learning

An abnormality detector for a manufacturing machine, the abnormality detector comprising:

• a processor configured to:
• cause to output a signal in response to a detection of a sign of an abnormality in the manufacturing machine based on a physical quantity acquired from
• (1) the manufacturing machine,
• (2) a sensor installed in the manufacturing machine or
• (3) a sensor installed in a vicinity of the manufacturing machine,
• observe, as a state variable representing a present state of an environment, physical quantity data indicating the physical quantity related to an operation of the manufacturing machine from the manufacturing machine, the sensor installed in the manufacturing machine, or the sensor installed in the vicinity of the manufacturing machine,
• acquire, as label data, operation state data indicating an operation state of the manufacturing machine,
• learn the operation state of the manufacturing machine with respect to the physical quantity related to the operation of the manufacturing machine, using the state variable and the label data, and
• estimate the operation state of the manufacturing machine using a learned result, based on the physical quantity related to the operation of the manufacturing machine, and output an estimation result,
• wherein, even when a sign of an abnormality in the manufacturing machine has been detected, the processor is configured to still suspend output of the signal, according to the output estimation result of the operation state of the manufacturing machine,
• wherein the processor is further configured to
• calculate an error between (i) a correlation model for estimating the operation state of the manufacturing machine based on the physical quantity obtained from the manufacturing machine, the sensor installed in the manufacturing machine, or the sensor installed in the vicinity of the manufacturing machine, and (ii) a correlation feature identified from teacher data, which is obtained from the physical quantity obtained from the manufacturing machine, the sensor installed in the manufacturing machine, or the sensor installed in the vicinity of the manufacturing machine in the past and a confirmation result of the operation state of the manufacturing machine at that time, and
• update the correlation model such that the error between the correlation model and the correlation feature is reduced, and repeat updating of the correlation model, thereby bringing the correlation model closer to an optimal model according to the environment.