Tong Weiming, Shen Jiaqi, Li Zhongwei, Chu Xu, Jiang Wenqi, Tan Liguo
Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China.
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2024 Aug 22;24(16):5433. doi: 10.3390/s24165433.
Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model.
准确识别刀具状态对于最大化刀具寿命至关重要。然而，在实际场景中收集的刀具传感器数据具有不平衡的特征。此外，尽管图神经网络（GNN）在数据空间维度的特征提取方面表现出色，但难以有效地在时间维度提取特征。因此，我们提出了一种基于剪枝优化图神经网络-门控循环单元（POGNN-GRU）的不平衡数据下刀具状态识别方法。首先，通过引入自适应噪声去除策略并改进多数加权少数过采样技术（MWMOTE）来设计改进的多数加权少数过采样技术（IMWMOTE），以缓解数据的不平衡问题。随后，提出一种基于多尺度多度量基础和高斯核权重函数的POG图数据构建方法，以解决单度量基础下图数据的片面描述问题。然后，构建POGNN-GRU模型以深度挖掘数据的空间和时间特征，从而更好地识别刀具状态。最后，在PHM 2010和HMoTP数据集上进行的验证和消融实验表明，所提出的方法在识别方面优于其他模型，与相应的最优基线模型相比，最高准确率提高了1.6%和1.86%。
