An abnormality determination apparatus, which determines abnormality of a temperature sensor in a machine tool, includes: a temperature pattern storage unit which stores a normal pattern indicating temperature change relative to machining time for a machining classification of the machine tool; a temperature data acquisition unit which acquires temperature data outputted by the temperature sensor provided to the machine tool; a machining classification determination unit which determines a machining classification of the machine tool; a comparison data acquisition unit which extracts the normal pattern relative to a determined machining classification from the temperature pattern storage unit; a comparison unit which compares the extracted normal pattern, and as acquired pattern indicating temperature change relative to machining time according to the acquired temperature data; and an abnormality determination unit which determines abnormality of a temperature sensor based on a comparison result.

There is provided a system that includes a review tool configured to review at least part of potential defects of an examined object, and assign each of the at least part of the potential defects with a multiplicity of attribute values. The system also includes a computer-based classifier configured to classify, based on the attribute values as assigned, the at least part of potential defects into a set of classes, the set comprising at least a first major class, a second major class and a first minor class, the classifier trained based on a training set comprising a multiplicity of training defects with assigned attribute values, the training defects classified into the set of classes.

There is provided a system that includes a review tool configured to review at least part of potential defects of an examined object, and assign each of the at least part of the potential defects with a multiplicity of attribute values. The system also includes a computer-based classifier configured to classify, based on the attribute values as assigned, the at least part of potential defects into a set of classes, the set comprising at least a first major class, a second major class and a first minor class, the classifier trained based on a training set comprising a multiplicity of training defects with assigned attribute values, the training defects classified into the set of classes.

An abnormality determination apparatus, which determines abnormality of a temperature sensor in a machine tool, includes: a temperature pattern storage unit which stores a normal pattern indicating temperature change relative to machining time for a machining classification of the machine tool; a temperature data acquisition unit which acquires temperature data outputted by the temperature sensor provided to the machine tool; a machining classification determination unit which determines a machining classification of the machine tool; a comparison data acquisition unit which extracts the normal pattern relative to a determined machining classification from the temperature pattern storage unit; a comparison unit which compares the extracted normal pattern, and as acquired pattern indicating temperature change relative to machining time according to the acquired temperature data; and an abnormality determination unit which determines abnormality of a temperature sensor based on a comparison result.

There are provided a system, computer software product and method of generating a training set for a classifier using a processor. The method comprises: receiving a training set comprising training defects each having assigned attribute values, the training defects externally classified into classes comprising first and second major classes and a minor class; training a classifier upon the training set; receiving results of automatic classification of the training defects; automatically identifying a first defect that was externally classified into the first major class and automatically classified into the second major class; automatically identifying by the processor a second defect from the multiplicity of training defects that was externally classified into the minor class and automatically classified to the first or second major classes; and correcting the training set to include the first defect into the second major class, or to include the second defect into the first or the second major class.

There are provided a system, computer software product and method of generating a training set for a classifier using a processor. The method comprises: receiving a training set comprising training defects each having assigned attribute values, the training defects externally classified into classes comprising first and second major classes and a minor class; training a classifier upon the training set; receiving results of automatic classification of the training defects; automatically identifying a first defect that was externally classified into the first major class and automatically classified into the second major class; automatically identifying by the processor a second defect from the multiplicity of training defects that was externally classified into the minor class and automatically classified to the first or second major classes; and correcting the training set to include the first defect into the second major class, or to include the second defect into the first or the second major class.

An anomaly determination method, in a production management system that manages a production line including first, second, and third production devices, includes: obtaining, from among first, second, and third operating status data of the first, second, and third production devices, respectively, at least the first and third operating status data; when determined that the second operating status data was not obtained, predicting a dummy operation timepoint and a dummy wait time based on operation timepoints and wait times of preceding and subsequent processes and generating dummy operating status data corresponding to the second operating status data including the dummy operation timepoint and wait time; performing anomaly determination processing on production processes as a whole based on the first, third, and dummy operating status data; and outputting determination result information representing a result of the anomaly determination processing on a display device in the production management system.