Abstract
Aiming at low efficiency of existing sliding bearing load balance calculation, a new method that based on neural network proportional-integral-derivative (PID) algorithm is proposed for the first time, in which a compound control algorithm combining neural network and PID algorithm is applied. In this new method, the load error is taken as the input of the system, and the eccentricity of the bearing is used as the input of the controller, and the output of the system is the oil film force of the bearing. Comparing with traditional method, calculation results show that: the number of iterations calculated by neural network PID algorithm is less than traditional one and has higher efficiency and stronger adaptability under different loads.
Highlights
IntroductionLoad balance calculation is the basis for calculation of dynamic characteristics of sliding bearings, whose results will change with different load conditions
Sliding bearings have a wide range of applications in large machineries [1]
Load balance calculation is the basis for calculation of dynamic characteristics of sliding bearings, whose results will change with different load conditions
Summary
Load balance calculation is the basis for calculation of dynamic characteristics of sliding bearings, whose results will change with different load conditions. The researchers have done a lot of research on the sliding bearing [3, 4], which effectively reveals the lubrication characteristics of the bearing and its influencing factors, but there are relatively few research methods for the calculation of bearing load balance. The sliding bearing is taken as the research object, and the mathematical model of fluid lubrication is established. The idea of control system is introduced into the calculation of load balance, and the neural network PID algorithm is used to effectively solve the problem of low computational efficiency and poor adaptability of traditional methods, which has certain reference significance for subsequent research
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