부산대학교 도서관

Timely fault detection and identification (FDI) of soft manipulators are critical in the design of surgical systems to improve reliability. However, due to the intrinsic compliance of soft manipulators, their end effectors vibrate during the dynamic control process, which introduces noise into the measured signals and makes FDI of soft manipulators challenging. This paper proposes a novel method to accomplish these tasks based on Long Short Term Memory (LSTM) recurrent neural network. Based on LSTM network, a new Attention-based Noise Compensation (ANC) module is proposed to enable the network to filter the noise merged with signals input in a self-supervision manner. Moreover, weighted cross entropy loss is introduced to balance the normal and faulty samples in the training set. Of the 9930 samples presented to the model, 9489 are correctly diagnosed in less than 1.0 second, which implies that the method can learn the spatial and temporal dependence of the signals and distinguish the healthy modes from the faulty ones. Finally, we compare the ANC-based method with the vanilla LSTM method and the state-of-art Bruin et al. method. From the comparison, we conclude that the ANC-based method proposed in this paper not only shortens the time cost of the FDI process but also suppresses the sensitivity of diagnosis results to noise. Source code, pre-trained models and dataset are available on https://github.com/IRMVLab/ANC-LSTM-fault-detection.