Alternative multi-label imitation learning framework monitoring tool wear and bearing fault under different working conditions

Abstract

Bearings and tools are the important parts of the machine tool. And monitoring automatically the fault of bearings and the wear of tools under different working conditions is the necessary performance of the intelligent manufacturing system. In this paper, a multi-label imitation learning (MLIL) framework is proposed to monitor the tool wear and bearing fault under different working conditions. Specially, the multi-label samples with multiple sublabels are transformed into the imitation objects, and the MLIL develops a discriminator and a deep reinforcement learning (DRL) to imitate the feature from imitation objects. In detail, the DRL is implemented without setting the reward function to enhance the feature extraction ability of deep neural networks, and meanwhile the discriminator is used to discriminate the generations of DRL and imitation objects. As a result, the MLIL framework can not only deal with the correlation between multiple working conditions including different speeds and loads, but also distinguish the compound fault composed of coinstantaneous bearing fault and tool wear. Two cases demonstrate jointly the imitation ability of the MLIL framework on monitoring tool wear and bearing fault under different working conditions.