The Simulation Analysis and Experimental Study on the Temperature Field of Four Row Rolling Bearings of Rolling Mill under Non-Uniform Load Conditions.

As a key component of the rolling mill, the four-row cylindrical roller bearing (FCRB) operates under complex working conditions of high speed, high temperature, and heavy load. Due to the lack of an effective temperature test scheme for rolling mill bearings, a too high temperature can easily lead to bearing failure or damage under unsteady conditions. To reveal the internal temperature distribution law of four-row roller bearings of rolling mills and solve the common problem of difficult temperature monitoring of rolling mill bearings, in this paper, a four-row cylindrical roller bearing of 1140 mm cold rolling six-high mill is taken as the research object, and the temperature field calculation model for four-row cylindrical roller bearings is established. Firstly, the mechanical analysis model of FCRB is established on the basis of single row bearing by slice method. Secondly, the mechanical calculation model of FCRB is established by the Newton-Raphson method (NRM) and the finite element method (FEM). Thirdly, based on the mechanical calculation model, the finite element method is used to establish the temperature field model of FCRB under uniform load distribution and non-uniform load distribution. Finally, the temperature test experiment is carried out with the FCRB in the rolling mill fault diagnosis test bench. The results show that the error between the FCRB temperature calculation model and the experimental results is less than 10%. It can be seen that the uneven temperature distribution of FCRB is mainly caused by the uneven load distribution. The temperature distribution along the axial direction of the bearing is related to the load distribution of each column, while the circumferential temperature distribution is related to the azimuth angle.

Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS.

Rolling is the main process in steel production. There are some problems in the rolling process, such as insufficient ability of abnormal detection and evaluation, low accuracy of process monitoring, and fault diagnosis. To improve the accuracy of quality-related fault diagnosis, this paper proposes a quality-related process monitoring and diagnosis method for hot-rolled strip based on weighted statistical feature KPLS. Firstly, the process-monitoring and diagnosis model of strip thickness and quality based on the KPLS method is introduced. Then, considering that the KPLS diagnosis method ignores the contribution of process variables to quality, it is easy to misjudge the root cause of quality in the diagnosis process. Based on the rolling mechanism model, the influence weight of strip thickness is constructed. By weighing the statistical data features, a quality diagnosis framework of series structure data fusion is constructed. Finally, the method is applied to the 1580 mm hot-rolling process for industrial verification. The verification results show that the proposed method has higher diagnostic accuracy than PLS, KPLS, and other methods. The results show that the diagnostic model based on weighted statistical feature KPLS has a diagnostic accuracy of more than 96% for strip thickness and quality-related faults.