Jtekt Corporation

Koichi Kato

Toshihiro Yonezu

Hayato Komatsu

Yuki Ishigure

Natsuki Yabune

A life expectancy prediction system for a target tool includes a processing machine body, a detector to detect a state data, a learned model storage unit to store learned models generated by executing machine learning using training datasets, including an explanatory variable and an objective variable, the explanatory variable being the state data and the objective variable being a number of first remaining machining times, the learned model storage unit being to store the learned models, each for each of the tools and a remaining machining times prediction unit to select, based on the state data, one learned model and predict a number of second remaining machining times, using the one learned model and the state data.

Life expectancy prediction system for a tool

While a production apparatus is operating, a computation unit of an analysis device performs main predictive computation on an acceptability in real time by using one or more operating predictors selected from among a plurality of predictors. When the main predictive computation is not performed, the computation unit performs subsidiary predictive computation on the acceptability by using one or more subsidiary predictors that are not selected from among the plurality of predictors. The computation unit performs processing of interchanging the operating predictors and the subsidiary predictors based on prediction accuracies of the operating predictors and prediction accuracies of the subsidiary predictors.

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Nagakute, Japan

Koichi Kato