Fault Diagnosis for Gas Turbine Rotor Using Actor-Critic Network

As a key component of gas turbine, gas turbine rotor often operates under high speed and variable working conditions, which is extremely prone to failure. Aiming at the problem of low fault diagnosis accuracy of gas turbine rotor under variable working conditions, a deep reinforcement learning method based on actor and critic is adopted. The ACTOR network is responsible for inputting fault state data, fault type action and updated error signal. The critic network is responsible for inputting current fault state and rewarding the next fault state. The time difference method is used to update the output of network parameters and output reward information and diagnosis results. The diagnostic ability of three kinds of gas turbine rotor test bench data is tested, and compared with other methods, the actor-critic model has good fault diagnosis ability and domain adaptation ability in the variable condition experiment, which can solve the actual variable condition fault diagnosis ability of gas turbine to a certain extent.