Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分...针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分解,获取模态分量。接着,将原始信号与这些模态分量分割为多个时间局部片段,并计算它们对应时间局部片段的相关熵值。然后,通过LWT算法处理弱相关分量,最后重构剩余分量以完成去噪过程。实验结果表明:在5 km的传感距离和10 m的空间分辨率的条件下,系统的信噪比达到了54.36 d B,同时均方根误差降低至0.091。展开更多
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
文摘针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分解,获取模态分量。接着,将原始信号与这些模态分量分割为多个时间局部片段,并计算它们对应时间局部片段的相关熵值。然后,通过LWT算法处理弱相关分量,最后重构剩余分量以完成去噪过程。实验结果表明:在5 km的传感距离和10 m的空间分辨率的条件下,系统的信噪比达到了54.36 d B,同时均方根误差降低至0.091。
