Auto correlation based elevator rope monitoring and fault detection approach with image processing

Abstract

Elevators are the means that people often use in everyday life. From the past until nowadays many elevators have been used in many areas. Elevator systems with the formation of high-rise buildings in recent years has become more important. Early diagnosis of faults that may occur in the elevator system is very important. In this study, an approach has been proposed to monitor and detect faults on elevator ropes. The proposed method is based on image processing and auto correlation. Images are taken with the cameras fixed to the elevator system. The position of the elevator rope is determined by extracting the edges on the images. Thus, the elevator rope is monitored in real time. The detected rope is cut off from the gray format image. The elevator rope is observed by applying auto correlation to the obtained image. It is converted into image signals by using auto correlation method. The difference signal is generated by using the obtained auto correlation signal. High values in the difference signal are detected as rope fault. The proposed fault detection approach is quite fast because it has a signal processing base.