Li Shuxun, Yuan Kang, Hou Jianjun, Meng Xiaoqi
School of Petrochemical Technology, Lanzhou University of Technology, Lanzhou 730050, China.
Machinery Industry Pump Special Valve Engineering Research Center, Lanzhou 730050, China.
Sensors (Basel). 2025 Sep 3;25(17):5451. doi: 10.3390/s25175451.
In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time-frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness.
在现代工业中，弹簧全启式安全阀是承压系统安全泄压的关键装置。其阀座密封面长期使用后易损坏，导致内部泄漏，造成安全隐患和经济损失。因此，快速准确地诊断其内部泄漏状态具有重要意义。在当前识别流体机械故障的方法中，基于模型的方法在参数确定方面存在困难。虽然数据驱动的卷积神经网络（CNN）在故障诊断领域具有很大潜力，但存在超参数选择依赖经验、对时间序列和多尺度特征捕捉不足以及缺乏对阀门内部泄漏类型识别研究等问题。为此，本研究提出一种基于高频FPGA数据采集和改进型CNN的安全阀内部泄漏识别方法。通过高频FPGA采集系统获取不同内部泄漏状态的声发射信号，经短时傅里叶变换得到二维时频图并输入改进模型。该模型采用泄漏整流线性单元（LReLU）激活函数增强非线性表达，引入随机池化防止过拟合，借助角蜥优化算法（HLOA）优化超参数，并集成双向门控循环单元（BiGRU）和选择性内核注意力模块（SKAM）增强时间特征提取和多尺度特征捕捉。实验表明，该模型对安全阀内部泄漏状态的平均识别准确率为99.7%，优于ResNet-18等对比模型。该方法为安全阀内部泄漏诊断提供了有效解决方案，且信号转换方法可推广至其他机械设备的故障诊断。未来，将探索轻量级网络与多源数据的融合，以提高实时性和鲁棒性。
