基于CS-BBO优化SVM的汽轮机转子故障诊断

为了提高汽轮机转子故障诊断的准确率和识别效率,提出了一种基于混沌的生物地理学优化算法(biogeography-based optimization with chaos,简称CS-BBO)和支持向量机(support vector machine,简称SVM)相结合的故障诊断方法。首先,将混沌理论引入到生物地理学优化算法(biogeography-based optimization,简称BBO)中,得到CS-BBO算法;其次,通过CS-BBO算法优化SVM得到诊断模型的最优参数,增强SVM的学习能力和泛化能力;最后,通过ZT-3转子试验台模拟汽轮机转子故障,利用得到的4种状态下的试验数据验证优化模型的有效性。结果表明:CS-BBO算法优化SVM的模型可以准确、高效地对汽轮机转子进行故障诊断;与BBO算法优化SVM模型相比,该方法的故障诊断准确率和识别效率更高。

Fault Diagnosis for Steam Turbine Rotor by Using Support Vector Machine Based on CS-BBO Optimization Algorithm

To improve the accuracy and efficiency of turbine rotor fault diagnosis, a new method of fault diagnosis based on the biogeography-based optimization with chaos (CS-BBO) and support vector machine (SVM) is introduced. Firstly, the chaos theory is introduced into the biogeography-based optimization algorithm (BBO), the CS-BBO algorithm is obtained. Then, the optimal parameters of the SVM diagnostic model are obtained through the CS-BBO algorithm, and optimization model enhances the learning ability and generalization ability of SVM. Finally, the validity of the optimization model is verified by the experimental data of 4 kinds of states from ZT-3 rotor test bench to simulate the turbine rotor fault. The results show that the optimized model of SVM obtained by CS-BBO algorithm can be used to diagnose the fault of the steam turbine rotor accurately and efficiently. Compared with the optimized SVM model, which is obtained by the biogeography-based optimization algorithm, the accuracy and efficiency of fault diagnosis of this method is higher.