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Generative adversarial one-shot diagnosis of transmission faults for industrial robots

Abstract:

Transmission systems of industrial robots are prone to get failures due to harsh operating environments. Fault diagnosis is of great significance for realizing safe operations for industrial robots. However, it is difficult to obtain faulty data in real applications. To migrate this issue, a generative adversarial one-shot diagnosis (GAOSD) approach is proposed to diagnose robot transmission faults with only one sample per faulty pattern. Signals representing kinematical characteristics were acquired by an attitude sensor. A bidirectional generative adversarial network (Bi-GAN) was then trained using healthy signals. Inspired by way of human thinking, the trained encoder in Bi-GAN was taken out to perform information abstraction for all signals. Finally, the abstracted signals were sent to a random forest for the one-shot diagnosis. The performance of the present technique was evaluated on an industrial robot experimental setup. Experimental results show that the proposed GAOSD has promising performance on the fault diagnosis of robot transmission systems.