机电一体化设备故障智能诊断技术探讨  

Three-Phase Asynchronous Motor Equipment Fault IntelligentDiagnosis Technology Discussion

作　　者：范梅林 Fan Meilin(Guizhou Ziyun Southwest Cement Co.,Ltd.,Anshun,Guizhou 561000,CHN)

机构地区：[1]贵州紫云西南水泥有限公司,贵州安顺561000

出　　处：《模具制造》2024年第12期230-232,共3页Die & Mould Manufacture

摘　　要：为提高机电一体化设备故障诊断精确性和效率,研究引入神经网络。采用数据驱动策略,通过传感器获取运行参数,进行去噪处理和特征筛选。使用TensorFlow深度学习框架训练模型,应用Softmax激活函数、反向传播、交叉熵损失函数和梯度下降算法。模拟轴承磨损、齿轮断裂、机电过热和控制系统故障进行验证。结果显示各故障类型诊断准确率均超96%,证实模型高效性和准确性,为工业自动化设备维护提供可靠支持。To improve the accuracy and efficiency of fault diagnosis in mechatronics equipment,the introduction of neural networks is studied.Adopting a data-driven strategy,operating parameters are obtained through sensors for denoising and feature selection.Train the model using the TensorFlow deep learning framework and apply Softmax activation function,backpropagation,cross entropy loss function,and gradient descent algorithm.Simulate bearing wear,gear fracture,mechanical and electrical overheating,and control system failures for verification.The results showed that the diagnostic accuracy of each fault type exceeded 96%,confirming the efficiency and accuracy of the model and providing reliable support for the maintenance of industrial automation equipment.

关 键 词：机电一体化设备 故障 智能诊断技术 

分 类 号：TG659[金属学及工艺—金属切削加工及机床]