Research on acoustic emission signal recognition of bearing fault based on TL-LSTM

Aiming at the issue of fault acoustic emission (AE) signal intelligent recognition of rolling bearing under multiple working conditions, a new fault recognition method combining long short-term memory (LSTM) networks and transfer learning (TL) is proposed. This method only takes the original AE signal parameters under single working condition as the training samples and constructs LSTM model to fully excavate the deep mapping relationship between AE signals and faults, so as to identify the faults under other working conditions that have similar distribution characteristics with the training working condition. TL is introduced and combined with the LSTM model to deal with the fault identification problem under other working conditions that have different distribution characteristics. Thus, the adaptive extraction and intelligent recognition of the fault features under various types of working conditions can be completed. The experiment results show that the recognition of inner ring, outer ring and cage faults have high accuracy under the working condition changes of the rotation speed, acquisition position and type of the rolling bearing. The real-time on-line intelligent monitoring task of the faults can be completed end-to-end under various types of working conditions. The proposed method gets rid of the over-reliance on prior fault data, and the feasibility and superiority of the proposed method are verified. © 2019, Science Press. All right reserved.