US 12,065,809 B2
Damage estimation device and machine learning device
Takaaki Izuka, Hiroshima (JP); Koji Sumimoto, Hiroshima (JP); and Katsuya Irieda, Hiroshima (JP)
Assigned to KOBELCO CONSTRUCTION MACHINERY CO., LTD., Hiroshima (JP)
Appl. No. 17/422,317
Filed by KOBELCO CONSTRUCTION MACHINERY CO., LTD., Hiroshima (JP)
PCT Filed Jan. 16, 2020, PCT No. PCT/JP2020/001365
§ 371(c)(1), (2) Date Jul. 12, 2021,
PCT Pub. No. WO2020/162136, PCT Pub. Date Aug. 13, 2020.
Claims priority of application No. 2019-021832 (JP), filed on Feb. 8, 2019.
Prior Publication US 2022/0090363 A1, Mar. 24, 2022
Int. Cl. E02F 9/26 (2006.01); E02F 3/90 (2006.01); E02F 3/92 (2006.01)
CPC E02F 9/267 (2013.01) [E02F 3/907 (2013.01); E02F 3/92 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A damage estimation device that estimates damage in a predetermined portion associated with an operation of a work machine, the damage estimation device comprising:
an operation parameter acquisition unit that acquires an operation parameter related to the operation of the work machine;
a damage estimation model storage unit that stores a damage estimation model constructed by machine learning using training data with the operation parameter as an input value and a damage parameter related to damage in the predetermined portion of the work machine as an output value; and
an estimation unit that estimates the damage parameter by inputting the operation parameter acquired by the operation parameter acquisition unit to the damage estimation model stored in the damage estimation model storage unit, wherein
the damage estimation model includes a plurality of damage estimation models different for each specification of the work machine,
the damage estimation model storage unit stores each of a plurality of specification parameters related to a specification of the work machine and each of the plurality of damage estimation models in association with each other,
the work machine includes a lower travelling body, an upper slewing body mounted on the lower travelling body, and a work device including a boom supported on the upper slewing body in a raising and lowering manner, an arm swingably coupled to a tip end of the boom, and a tip attachment attached to a tip end of the arm,
the specification parameter includes a combination of a length of the boom, a length of the arm, and a specification of the tip attachment,
the damage estimation model storage unit stores each of a plurality of combinations of a length of a boom, a length of an arm, and a specification of a tip attachment and each of the plurality of damage estimation models in association with each other,
the damage estimation device further comprises:
a specification parameter acquisition unit that acquires a combination of a length of a boom, a length of an arm, and a specification of a tip attachment of a work machine to be estimated; and
a selection unit that selects a damage estimation model associated with the the combination of the length of the boom, the length of the arm, and the specification of the tip attachment acquired by the specification parameter acquisition unit from among the plurality of damage estimation models stored in the damage estimation model storage unit, and
the estimation unit estimates the damage parameter by inputting the operation parameter acquired by the operation parameter acquisition unit into the damage estimation model selected by the selection unit.