Hou Chunming, Zheng Liaomo
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 Jun 25;24(13):4117. doi: 10.3390/s24134117.
Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages.
以往的研究主要集中在将预测工具的剩余使用寿命（RUL）作为一个独立的过程。然而，工具的RUL与其磨损阶段密切相关。鉴于此，提出了一种基于变压器编码器和定制门控（TECGC）的多任务联合学习模型，用于同时预测工具RUL和工具磨损阶段。具体来说，变压器编码器被用作TECGC模型的主干，用于从原始数据中提取共享特征。定制门控（CGC）用于提取与工具RUL预测和工具磨损阶段相关的特定任务特征以及共享特征。最后，通过整合这些组件，TECGC模型可以同时预测工具RUL和工具磨损阶段。此外，还为模型训练提出了一种动态自适应多任务学习损失函数，以提高其计算效率。这种方法避免了因损失函数权衡参数选择不合理而导致模型预测性能不佳的问题。使用PHM2010数据集评估了TECGC模型的有效性。结果证明了其准确预测工具RUL和工具磨损阶段的能力。
