الفهرس 
Title : A Deep Reinforcement Learning Approach for Active Vibration Control in Rotating Machinery
SummaryOnly 14 pages are availabe for public view
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Abstract
Over the past decade, deep reinforcement learning has greatly impacted the field of
continuous control, from mastering simple games to controlling multiple actuators in a robot
doing complex tasks. Deep reinforcement learning agents are capable of finding optimal
control policies without a model of the underlying system. On the other hand, in the field
of rotordynamics, vibration control has been previously achieved almost exclusively using
classical control theories that rely on modeling the system and diagnosing the cause of
vibration. Vibration control of rotating machinery is crucial to prevent failures and allow
machines to operate in dynamic vibration conditions or near their critical speeds. We propose
using model-free deep reinforcement learning to control multiple sources of vibrations
in a system supported by Smart Electro-Magnetic Actuator Journal Integrated Bearings
(SEMAJIB). SEMAJIB is a smart bearing that integrates a journal bearing for load carrying
and an electromagnetic actuator for control purposes. Journal bearings are excellent load
carriers; however, they introduce some instabilities known as oil whirl and oil whip due to
the movement of oil.
In this work, we demonstrate the ability of the proposed deep reinforcement learning
controller in finding successful control policies for stabilizing the system and reducing the
synchronous vibration caused by the rotor’s unbalance. Our proposed controller is evaluated
on a simulated and physical test rig with both unbalance and oil whip vibration. The proposed
controller is able to balance the system with unbalance vibration reduction of up to 93%. The
controller is able to completely eliminate oil whip vibration with a vibration reduction of up
to 99%. In a system with both vibrations, the proposed controller reduced the total vibration
by 85%.