Fanuc Corporation

Tomofumi Uwano

A machine learning device comprises: a video data feature extraction unit that extracts a feature of an action performed by a worker from video data; a training data extraction unit that assigns the video data with a label indicating work details of a specific action to extract training data; an operation data feature extraction unit that extracts a feature relevant to the extracted training data from operation data; a label assignment criteria creation unit that creates label assignment criteria for video data and operation data; a label assignment unit that assigns labels to unlabeled video data and operation data on the basis of the label assignment criterias for video data and operation data; and a training unit that executes machine learning using training data including labelled video data to generate a trained model for classifying work of a worker on the basis of video data.

Machine learning device, classification device, and control device

An analysis device according to the present disclosure is provided with: a data acquisition unit that acquires data detected by an industrial machine; an operation state extraction unit that extracts, from the data, data detected during operation of the industrial machine; an annotation unit that creates a plurality of data set groups by giving, on the basis of a predetermined standard, annotations indicating motion states of the industrial machine to a plurality of data sets segmented by a predetermined standard from the extracted data during the operation; a feature amount extraction unit that extracts a feature amount of data included in each of the data sets; a learning unit that generates decision tree models respectively for the plurality of data set groups; and a display unit that ranks the generated decision tree models on the basis of correct answer rates regarding prediction of the annotations based on predetermined training data and that displays the decision tree models.

Analysis device

An analyzer includes a preliminary analysis unit that extracts interval data obtained by cutting out time-series data with a predetermined sliding window width, and analyzes simplicity of a change trend of the extracted interval, a data division unit that divides the data into pieces of division data with a sliding window width set based on an analysis result performed by the preliminary analysis unit, a data generation unit that generates combination data which is a text indicating a change trend in the division data based on the division data, and a data analysis unit that analyzes the combination data.

Analyzer

A missing value imputation device for imputing a missing value in collected data includes: an imputation processing unit that measures a period of time required for imputation process by conducting a simulation, and associates the required period of time with each multiplexing number at which data is to be multiplexed through a multiple imputation technique; a multiplexing number acquisition unit, when an allowable period of time allowed for the imputation process is designated, the multiplexing number acquisition unit acquiring the multiplexing number at which the imputation process is to be finished within the allowable period of time, on the basis of the associated required period of time; and a calculation technique determination unit that determines a calculation technique for the imputation process in response to the acquired multiplexing number.

Missing value imputation device, missing value imputation method, and missing value imputation program

An abnormality detection parameter adjustment display device adjusts a parameter for determination for detecting operation abnormality of a mechanical facility based on an input from a user, collects operation information on the mechanical facility, creates a dataset based on the operation information, determines whether or not the dataset indicates a normal operating state, based on the parameter, displays the dataset in a graph, and makes an adjustment for display in such a position that the relationship between the data of the graph and the parameter is visually ascertainable.

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Yamanashi, Japan

Tomofumi Uwano