Adaptive Vision-Based Method for Rotor Dynamic Balance System

Abstract

This article presents a new adaptive vision-based method (AVBM) of performing automatic detection for rotor balancing, and the online implementation proved that the method achieved rapid real-time optimization of system balancing configuration for a rotor dynamic balance machine. The proposed AVBM integrated 3D sensors and dynamic balancing platform using 3D computer vision technique and dynamic balance algorithm to improve the efficiency of rotor dynamic balancing. AVBM applied 3D ToF sensors on active rotor dynamic balance machines to grab 3D point cloud of rotor shaft and balance sprues. By 3D depth data, the background noise can be removed to detect the positions of shaft center, key phasor and balance sprues of rotor automatically. After combining with unbalance vector from dynamic balancing machine, the AVBM system calculated the optimal balance configuration by the vector analysis algorithm. Compares to conventional methods, conventional rotor dynamic balancing process relies on technicians to mount washers on particular balance sprues based on their experience, therefore uncertainty causes productivity decline. Experiments in industrial examples showed that the proposed AVBM required fewer rounds to achieve acceptance, whereas the conventional industrial rotor balancing method performed by operators required more than three rounds in average. Consider the overall dynamic balancing process for the motor, the processing time required for each motor without AVBM was 348.9 seconds, and the daily rotor balancing count of each dynamic balancing machine was 83. The processing time with AVBM was shortened from 348.9 to 283.9 seconds, the daily rotor balancing count had increased from 83 to 101, and the production improvement had reached 22 %. That is, the proposed AVBM can accurately analyze the dynamic balance and greatly reduce the redundant dynamic balance operations. The main advantage of the proposed AVBM over conventional methods is its efficiency, effectiveness and robustness in online optimization of rotor dynamic balance.