Wheel State Monitoring System For Sinter Cooler Pallet Based On Machine Vision

Sinter cooler is the bearing for cooling in sintering process,and it is one of the important equipment of ferrous metallurgy.The rugged working condition of the sinter cooler makes the bearing of the trolley wheel easy to deform and wear,causing the wheel to fall off from the axle,seriously affecting the production efficiency and threatening the safety of the personnel.Therefore,it is of great significance to study wheel state early warning mechanism and realize automatic monitoring.In the process of bearing failure,the wheel changes from pure rolling to sliding on the rails,and the rotational speed decreases.Using this feature,the speed measurement of the trolley wheel is studied.The main tasks of the paper include:5.Design the overall network architecture based on technical indicators and system requirements.The camera and sensor collect data online,PLC coordinate the controls and the central server to achieve real-time image processing and alarm mechanism.6.Calibrate the camera by 2 step based on the Zhang's method to eliminate the distortion and extend the sampling range of rolling wheels.The improved histogram equalization and Hough linear detection are applied to eliminate the influence of illumination change and projection.An adaptive threshold Canny's algorithm is proposed,which is applied to the Hough circle transform to extract the rotating region of the wheel.7.A rotational speed measurement method based on image feature matching is proposed.The ORB algorithm is used to match the markers on the wheel in two consecutive frames,and affine transformation matrix is estimated for the filtered matching point pairs to obtain a pair of instantaneous velocities--translational and rotational--of rolling,on which based the rolling/sliding state of the wheel can be judged.End systems are alerted when the sliding momentum exceeds the threshold.The sequence number is extracted and identified based on contour extraction and template method to recognize the specific wheel.8.Detect wheels falling off fault based on photoelectric sensors and speed integral method.The wheel arrival signal is detected by photoelectric switch,and the arrival time is predicted according to the travel speed of the trolley,and the failure of the wheel is detected by redundancy mechanism.Based on the above algorithms and ideas,a wheel condition monitoring system for sinter cooler based on machine vision is developed to realize on-line detection,fault prediction and fault tracking of wheel status.This system has been successfully verified on no.4 sinter cooler in Baosteel ironworks.The instantaneous speed detection takes less than 300ms,the error is less than 5%,the average speed error is less than 2%,and the comprehensive fault detection rate is up to 99.5%.