A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning.

ABSTRACT: Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we proposed a new method to diagnose the abnormal wear state of the disc cutter by using brain-like artificial intelligence to process and analyze the vibration signal in the dynamic contact between the disc cutter and the rock. This method is mainly aimed at realizing the diagnosis and identification of the abnormal wear state of the cutter, and is not aimed at the accurate measurement of the wear amount. The author believes that when the TBM is operating at full power, the cutting forces are very high and the rock is successively broken, resulting in

SUBMITTER: Pu X 

PROVIDER: S-EPMC9459918 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature