Reducing Vibration of A Rotating Machine with Deep Reinforcement Learning

Reducing vibration of a high-speed rotating machine is an important task as the vibration can damage equipment, increase costs and reduce quality. Faced with the problems, in this paper, we proposed a control tool with several pads and sensors to actively dampening the vibration of the machine. Using Deep Reinforcement Learning (RL) to learn a policy that determines whether to stretch out a pad and the speed of the pads according to the information collected by sensors, our approach can change the displacement of the rotating machine in space by generating an opposite force with a pad while it touches the wall so as to reduce the vibration. We evaluate our approach against two baselines, a control method with all the pads stretched out and a method without equipping the control tool. Our results indicate that simple RL algorithms in controlling the pad tool have effectiveness in reducing the vibration of the machine.