Research on Predicting the Wear Degree of Elevator Brake-shoe Based on Digital Twin Technology

Block brake is the key equipment to ensure the safe operation of the passenger elevator. In the long-term work, the brake performance of the brake shoe is gradually degraded, which affects the safety of the elevator. This paper presents a digital twin monitoring method for block brake shoe wear, and predicts the remaining life of brake shoe based on the digital twin model. The brake vibration signal is collected, the vibration signal is converted into image signal by Gram Angle field (GAF), the brake shoe wear is identified by machine learning, and the brake shoe finite element model is reconstructed according to the identification results of the wear amount, so as to realize the synchronous mapping between the block brake shoe and the virtual digital model. The results show that the method has good feasibility, can monitor the wear state of brake shoe in real time, and display the wear position distribution of brake shoe, which provides a new method for intelligent monitoring level of elevator.