1.北京信息科技大学机电工程学院北京100192;2.北京信息科技大学高端装备制造智能感知与控制北京市国际科技 合作基地北京100192;3.北京信息科技大学现代测控技术教育部重点实验室北京100192
TN07;TK477
北京市自然科学基金（IS24076，21JC0016）项目资助
Ma Kang
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception & Control Beijing International Science Technology Cooperation Base, Beijing 100192, China; 3.Key Laboratory of Mordern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, ChinaWang Hongjun
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception & Control Beijing International Science Technology Cooperation Base, Beijing 100192, China; 3.Key Laboratory of Mordern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, ChinaQiao Qi
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception & Control Beijing International Science Technology Cooperation Base, Beijing 100192, China; 3.Key Laboratory of Mordern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, ChinaWang Zheng
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception & Control Beijing International Science Technology Cooperation Base, Beijing 100192, China; 3.Key Laboratory of Mordern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, ChinaYu Chenglong
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception & Control Beijing International Science Technology Cooperation Base, Beijing 100192, China; 3.Key Laboratory of Mordern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, China1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception & Control Beijing International Science Technology Cooperation Base, Beijing 100192, China; 3.Key Laboratory of Mordern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, China
针对燃气轮机转子系统故障样本少，样本不均以及跨工况故障诊断的问题，提出一种基于转子系统数模联动的故障诊断方法。基于经典集中质量法建立了转子系统动力学模型，将不对中故障，不平衡故障引入动力学模型建立了转子系统故障动力学微分方程，最后通过龙格库塔法(ode-4/5)对转子系统故障动力学微分方程进行求解，最终得到故障位移仿真信号，为之后数据增强以及数模联动方法做准备。建立结合正交梯度惩罚算法的条件深度卷积生成对抗网络，用此模型将机理模型所得仿真信号作为生成器输入，生成信号样本与真实实验信号作为判别器输入，获得融合机理特性与实际机械特性为一体的生成信号;其次，基于迁移学习理论建立跨工况域适应故障诊断模型，使用结合反卷积算法的短时分数阶傅里叶变换对数据进行视频转换，获得分辨率以及特征更为明显的二维时频图像样本，将结合机理特性与机械特性的数据作为源域、待测其他工况数据作为目标域训练故障诊断模型，通过实验验证，将5种不同故障类别在不同转速与不同故障量下的诊断准确率均达到91%以上，并通过混淆矩阵对结果进行了解释分析，该方法能够有效提高模型的泛化性，并实现转子系统跨工况故障诊断，同条件下优于其他主流诊断方法。
Aiming to address the problems of limited sample size, uneven sample distribution, and cross-operating condition fault diagnosis for gas turbine rotor systems, a fault diagnosis method based on the linkage of numerical model and physical model is proposed. A classical central mass method is used to establish the dynamic model of the rotor system, and fault dynamics differential equations are established in the model by introducing misalignment fault and unbalance fault. Finally, the differential equations of rotor system fault dynamics are solved by the Runge-Kutta method (ode-4/5), and the simulated signal of fault displacement is obtained, which is prepared for subsequent data augmentation and model linkage methods. A conditional deep convolutional generative adversarial network (GAN) model is established by combining the orthogonal gradient penalty algorithm, and the model is used to generate signals by inputting the simulated signals obtained from the physical model as the generator input, and inputting the real experimental signals as the discriminator input to obtain a generated signal that integrates the intrinsic characteristics of the physical model and the actual mechanical characteristics. Secondly, a cross-operating condition domain adaptation fault diagnosis model is established based on the theory of transfer learning, and the data is converted into a two-dimensional temporal-frequency image sample using a combined short-time fractional Fourier transform and inverse convolution algorithm, which provides more obvious resolution and features. The data that integrates the intrinsic characteristics of the physical model and the mechanical characteristics is used as the source domain and the other target domain data to be measured at other operating conditions is used to train the fault diagnosis model. The experimental verification shows that the diagnostic accuracy of the five different fault categories at different speeds and fault levels in different operating conditions is all above 91%. The results are explained and analyzed through confusion matrix, and the method can effectively improve the model’s generalization ability and realize cross-operating condition fault diagnosis of rotor systems, which is superior to other mainstream diagnosis methods under the same conditions.
马康,王红军,乔琦,王正,余成龙.转子系统数模联动故障诊断方法研究[J].电子测量与仪器学报,2025,39(1):165-175
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