Publication Date:
2018-11-30
Description:
The fault detection method based on the analytical model can not completely deal with the model uncertainty, disturbance torque, measurement error and other disturbances, and it is difficult to detect small faults similar to disturbances. A detection method of small actuator fault based on stacked autoencoder(SAE) network is proposed. By learning historical data of system, SAE network can reconstruct the state data with stable errors. The relation between states of system will change when a fault occurs, and residual will change too. The variation trend of the residual can be used to detect the fault. Simulation results show that SAE network is more robust to disturbance compared with Elman neural network and nonlinear observer. Small faults under the disturbance can be detected by SAE network.
Print ISSN:
1757-8981
Electronic ISSN:
1757-899X
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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