Abstract: A selection support device that supports selection of a work machine to be used for work from among a plurality of work machines, the selection support device including: a history information acquisition unit configured to acquire history information on a use history of each work machine; a specifying unit configured to specify a degree of use of each work machine based on the history information; and a selection unit configured to preferentially select a work machine with a small degree of use as a work machine to be used for work.

Abstract: The storage unit stores history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other. The acquisition unit acquires area information regarding a current work area. The identification unit identifies a degree of association between the area information acquired by the acquisition unit and the history information stored in the storage unit. The output unit outputs information included in the history information with priority according to the degree of association identified by the identification unit.

Abstract: The acquisition unit acquires area information regarding a current work area. The prediction unit predicts a work time in the current work area on the basis of the area information acquired by the acquisition unit and history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other.

Abstract: Inspection is efficiently performed without lowering inspection accuracy, by performing an inspection using AI processing. An inspection device has a learning unit that generates a learning model by performing learning for discriminating a type of an inspection object by using as teacher data at least a part of classification results obtained by classifying a plurality of inspected objects of a same type as an inspection object into a plurality of types, or acquires the learning model, a calculation unit that outputs numerical data obtained by quantifying a level of classification accuracy of the type of the inspection object, based on a result calculated by inputting the inspection object to the learning model, and a determination unit that determines, by comparing the numerical data with types of thresholds, whether to automatically discriminate the type of the inspection object or to manually discriminate the type of the inspection object.

Abstract: A gear inspection apparatus includes a distance measurement device, a value search part, a difference calculation part, a selecting part, a judgment value determination part, and a judgment part that are connected together in the named order. A raw waveform obtained by the distance measurement device is converted by the value search part into a rectangular waveform of maximum value and a rectangular waveform of minimum value for each prescribed number of gear teeth, and a difference between the maximum value and the minimum value is calculated by the difference calculation part. The difference is converted into a nick determination value by the judgment value determination part, and the presence or absence of a nick is determined by the judgment part.

Abstract: A gear inspection apparatus includes a distance measurement device, a value search part, a difference calculation part, a selecting part, a judgment value determination part, and a judgment part that are connected together in the named order. A raw waveform obtained by the distance measurement device is converted by the value search part into a rectangular waveform of maximum value and a rectangular waveform of minimum value for each prescribed number of gear teeth, and a difference between the maximum value and the minimum value is calculated by the difference calculation part. The difference is converted into a nick determination value by the judgment value determination part, and the presence or absence of a nick is determined by the judgment part.