Offset Detection of Grate Trolley’s Side Plate Based on YOLOv4

Abstract

Side plate offset is one of the grate system faults. If it is not dealt with in time, some accidents will occur and economic losses will be made. Aiming at the problems like time-consuming, labour-wasting, and low intelligent by the side plate offset detection method manually, an autoside plate offset detection method is proposed, based on You Only Look Once version 4 (YOLOv4). Two cameras were fixed to collect the image information of the grate trolley’s side plate. With reference to the grate trolley’s operation, the offset judgment rules were set. YOLOv4 object detection algorithm was used to detect the side plate and trolley’s chassis frame in video frame images. A baseline was set according to the position information of the trolley’s chassis frame output by detection, and then, the position intervals between side plates and the baseline could be determined by calculation. According to the judgment rules, the scheme in this paper could detect the offset fault of the trolley’s side plate timely, and an alarm would be made automatically when faults are detected. Our video images of the trolley’s side plate were collected and sorted in Baogang Group sintering plant for testing. In this experiment, no error judgment was made, and the average detection and judgment time was 0.024 s. In this paper, rather than manually, the real-time automatic detection was realized to detect the offset fault of the trolley’s side plate so as to provide a new solution for offset detection of the grate trolley’s side plate.

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

“…In addition, relying on the development of machine vision-related technologies, noninvasive detection of specific target faults can also be achieved by processing image information streams in real time [58]. Lim et al [59] proposed a thermal-image-based fault diagnosis method by acquiring thermal images of rotating machinery using an infrared thermography camera while acquiring the vibration signal of the rotating machinery and considering the thermography signature of CIELab space as a pattern recognition paradigm.…”