Toyota Jidosha Kabushiki Kaisha

Tetsuya Yoshikawa

Hideaki Bunazawa

Kenichi Yamaguchi

Kei Yuasa

Tetsuo Hori

Hideto Watanabe

Hiroyuki Tsukamoto

Masanori Shimizu

The technology disclosed in the present specification is embodied as a support device for supporting maintenance on an apparatus including an expendable component. The support device includes at least one computer. The at least one computer executes: a process of acquiring an index indicative of a consumption level of the expendable component; a process of, at a time point when the index indicative the consumption level reaches a predetermined threshold, specifying a necessary period of time required for replacement of the expendable component at the time point; and a process of restricting an operation of the apparatus when a remaining period of time of the expendable component, the remaining period of time being associated with the threshold, is shorter than the necessary period of time required for the replacement.

Support device

An abnormality determination device is applied to a vehicle provided with a transmission configured to transmit power by rotation of a shaft. The abnormality determination device includes a processor and a memory. The memory configured to store mapping data that is data that defines mapping learned by machine learning. The processor is configured to execute an acquisition process and a determination process. The acquisition process is a process of acquiring a variable indicating a time-series data of a rotation speed of the shaft and using the variable as a value of an input variable of the mapping. The determination process is a process of determining whether an abnormality has occurred in the transmission based on a value of the output variable acquired using the value of the input variable and the mapping.

Abnormality determination device

An anomaly determination apparatus for a vehicle that includes a transmission includes an execution device and a memory device. The memory device is configured to store map data that defines pre-trained map, which has been trained through machine learning. When a variable representing time-series data of a rotation speed of a gear is fed to the map as an input variable, the map outputs a state variable representing a state of the gear as an output variable. The execution device is configured to execute: an obtaining process that obtains the variable representing the time-series data as a value of the input variable; and a determination process that determines whether there is an anomaly in the transmission based on a value of the output variable that is output by the map when the value of the input variable is fed to the map.

Anomaly determination apparatus

A storage device stores a mapping data entry that specifies a mapping and includes data learned through machine learning. A detection value of an oil temperature sensor is an oil temperature detection value. When oil temperature relation data, which is data corresponding to time series data of the oil temperature detection value, is input to the mapping as an input variable, the mapping outputs an output variable that determines whether a frictional engagement element has an anomaly. Processing circuitry executes an obtainment process that obtains the input variable and an anomaly determination process that determines whether the frictional engagement element has an anomaly based on the output variable that is output from the mapping as a result of inputting the input variable obtained in the obtainment process into the mapping.

Growing community of inventors

Toyota, Japan

Tetsuya Yoshikawa