Yingchao Liu
ABSTRACT
Condition-based maintenance and the prediction of the remaining useful life (RUL) of cutting tools are of crucial importance to reduce unexpected downtime and ensure quality. Our paper proposes a deep adversarial transfer learning based approach for RUL prediction of cutting tools. It mainly includes three parts: source domain pre-training, adversarial domain adaption and target domain prediction. Firstly, we pre-train a source long short-term memory (LSTM) network and a nonlinear regression model by using the labeled source cutting tool examples. Secondly, we perform adversarial domain adaption by learning a target LSTM model that minimize the distance between the source domain and target domain under their respective mapping, thus making it impossible for the discriminator to distinguish between the target and source cutting tools. Finally, the RUL of target cutting tools can be predicted. Our proposed method is applied to the data obtained the data obtained from a turbine factory's slotting cutter machining process. The result shows that the effectiveness and practicability of our proposed method.
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Index Terms
- Remaining Useful Life Prediction of Cutting Tools based on Deep Adversarial Transfer Learning
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