A Proposal of Remaining Useful Time Prediction Utilizing Operation Data of Construction Machinery

Abstract

For users to carry out various jobs according to the construction plan, if unexpected machine failures occur and their machines go down for an extended time, they will be huge losses. Therefore, machine maintenances are required for their machines to prevent from machine failures. However, due to operation in severe environment condition such as high load or long time use and in unexpected use, they often fail earlier than expected. For the maintenance of construction machinery, we propose to detect early indications of failure by predicting remaining useful times. Thereby, their machines can be performed maintenance before their failures and prevent unexpected machine failures. We propose to predict the machine failures of lower traveling bodies of hydraulic excavators by estimating their remaining useful times. Moreover, we also propose a practical example of maintenance activity using remaining useful times prediction in addition to failure prediction by neural network for hydraulic excavators and its effectiveness is shown.