转子裂纹深度自适应预测与故障诊断  

Adaptive Prediction of Rotor Crack Depth and Fault Diagnosis

作　　者：左刚奥 包文杰 陈志昊 李富才 ZUO Gang'ao;BAO Wenjie;CHEN Zhihao;LI Fucai(School of Mechanical Engineering,Shanghai Jiao Tong University Shanghai,200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《振动．测试与诊断》2024年第4期660-667,823,共9页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(52175104);船舶动力基础科研计划资助项目(M-M0709)。

摘　　要：针对传统的转子结构裂纹故障识别方法中特征提取困难、无法定量识别裂纹深度及受噪声污染严重的问题,提出了一种基于转子轴心轨迹的转子裂纹深度预测模型。该模型基于奇异值分解和卷积降噪自编码器(singular value decomposition-denoising convolutional autoencoder,简称SVD-DCAE),能够有效提取裂纹转子的故障特征并准确预测转子裂纹的扩展阶段。将裂纹转子的轴心轨迹作为模型的输入,分别使用仿真数据和实验数据训练和验证模型,并在仿真数据和实验数据中添加随机噪声模拟不同噪声环境。结果显示:所提出模型能够实现转子裂纹扩展程度的准确预测,在弱噪声环境中(信噪比为10 dB)裂纹深度预测准确率高于98%;具有较强的抗噪声能力和鲁棒性,在强噪声环境中(信噪比为-10 dB)预测准确率达到80%,远高于其他经典的卷积神经网络预测模型。Aiming at the problems existing in traditional identification methods of rotor crack fault such as difficulty in feature extraction,inability to quantitatively identify the crack depth and susceptibility to noise pollution,a prediction model of crack depth based on axis orbit is proposed.The proposed model is based on singular value decomposition and denoising convolutional autoencoder(SVD-DCAE),which can effectively extract the fault characteristics of the cracked rotor and accurately predict the crack growth stage.The center orbits of the cracked rotor are considered as the input of the proposed model.Simulated data and experimental data are used to train and verify the proposed model respectively,and random noise is added to simulate different noise environments.The results suggest that the SVD-DCAE model can realize the accurate prediction of the crack stage.In the weak noise environment(signal-to-noise ratio(SNR)of 10 dB),the prediction accuracy of the crack stage is higher than 98%.Meanwhile,SVD-DCAE possesses strong anti-noise ability and robustness.In the strong noise environment(SNR of-10 dB),the prediction accuracy of the crack stage still reaches 80%,which is much higher than other classic convolutional neural network prediction models.

关 键 词：裂纹转子 裂纹深度预测 轴心轨迹 奇异值分解 

分 类 号：TH133.2[机械工程—机械制造及自动化]