In the actual maintenance support application, it is difficult to carry out maintenance work according to the actual environment and conditions of the equipment to be repaired, which leads to the inaccurate timing of the equipment and the low efficiency of the equipment use. In order to study the relationship between the time interval of equipment repair and the geographical environment of the equipment more deeply, a RCNN deep learning model is proposed for training and feature extraction of equipment maintenance service information and geographical environment information, which can also predict the equipment maintenance interval. It also helps to compare among different prediction models and verify the effectiveness of the model, which further provides a method for the optimization of equipment maintenance interval.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.