Zeng Chao, Wang Chengchao, Xiong Xueqin, Wang Xiangjiang, Xiao Sheng
School of Nuclear Science and Technology, University of South China, Hengyang, China.
Hunan Metallurgical Planning and Design Institute Co., Ltd., Changsha, China.
PLoS One. 2025 Jun 18;20(6):e0326044. doi: 10.1371/journal.pone.0326044. eCollection 2025.
Monitoring the status of circular saw blades is an effective measure to ensure the production efficiency and safety of spent fuel assembly cutting. However, the prediction of wear during the cutting of stainless steel shells of spent fuel assemblies by circular saw blades is not entirely accurate in complicated working conditions. The main challenges include deficiency of data acquisition, signal features extracting complex process, and insufficient robustness of models. To enhance the precision of forecasting consequence, a circular saw blade wear prediction method combining generative adversarial network (GAN) and CNN-LSTM models is proposed. The main fault types during the cutting of stainless steel shells by circular saw blades should be identified in advance, and vibration signals of each fault state is able to be collected then. The collected data is supposed to be preprocessed through sampling overlapping, single-layer wavelet transform denoising, and normalization. GAN optimized by the Pearson correlation coefficient (PCC) has been utilized aiming to expand the data volume of each fault state to 300 samples and resulting in a total data volume of 2100 samples; A CNN-LSTM model based on dual feature fusion has been established to identify the wear status of circular saw blades, achieving an accuracy rate of 100%, higher than Long Short-Term Memory (LSTM) neural networks (86.2%) and Radial Basis Function Neural Networks (RBFNNs) (94.9%). This study effectively solves the problem of small sample sizes for circular saw blade wear data, and provides an efficient and accurate method for circular saw blade wear identification under complex working conditions, which has important practical significance for improving the safety and efficiency of spent fuel assembly cutting.
监测圆锯片的状态是确保乏燃料组件切割生产效率和安全性的有效措施。然而，在复杂工况下，圆锯片切割乏燃料组件不锈钢外壳时的磨损预测并不完全准确。主要挑战包括数据采集不足、信号特征提取过程复杂以及模型的鲁棒性不足。为提高预测结果的精度，提出了一种结合生成对抗网络（GAN）和CNN-LSTM模型的圆锯片磨损预测方法。应预先识别圆锯片切割不锈钢外壳过程中的主要故障类型，然后采集每种故障状态的振动信号。采集到的数据应通过采样重叠、单层小波变换去噪和归一化进行预处理。利用皮尔逊相关系数（PCC）优化的GAN将每种故障状态的数据量扩展到300个样本，总数据量达到2100个样本；建立了基于双特征融合的CNN-LSTM模型来识别圆锯片的磨损状态，准确率达到100%，高于长短期记忆（LSTM）神经网络（86.2%）和径向基函数神经网络（RBFNNs）（94.9%）。本研究有效解决了圆锯片磨损数据样本量小的问题，为复杂工况下圆锯片磨损识别提供了一种高效准确的方法，对提高乏燃料组件切割安全性和效率具有重要实际意义。
