基于CEEMDAN与CBBO-SVM的汽轮机转子故障诊断研究

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CEEMDAN(自适应噪声完备集合经验模态分解)和CBBO(混沌生物地理学优化算法)优化SVM(支持向量机)相结合的故障诊断方法。首先利用CEEMDAN对转子振动信号进行分解,提取PE(排列熵)作为故障特征值,并构造特征向量;其次将混沌理论引入到BBO(生物地理学优化算法)中,得到CBBO,通过CBBO优化SVM得到诊断模型的最优参数。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与先进性。结果表明:CBBO优化SVM模型可以准确、高效地对汽轮机转子进行故障诊断;与CPSO(混沌粒子群算法)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。

Study of the Diagnosis of the Faults Occurred to the Rotor of a Steam Turbine Based on the CEEMDAN(complete ensemble empirical mode decomposition with adaptive noise) and CBBO SVM(biogeography based optimization with chaos supporting vector machine)

To enhance the accuracy and identification efficiency to diagnose any faults in the rotor of a steam turbine,proposed was a method for diagnosing any faults based on a combination of the CEEMDAN and CBBO SVM.Firstly,the CEEMDAN was employed to perform a decomposition of the vibration signals from rotors,extract the permutation entropy (PE) as the fault characteristic values and create the characteristic vectors.Subsequently,the chaos theory was introduced into the BBO to obtain a CBBO based algorithm and the optimum parameters of the diagnostic model by optimizing the SVM through the adoption of the CBBO.Finally,the fault of the rotor of the steam turbine was simulated on the ZT-3 rotor test rig and the test data obtained in the four states were utilized to verify the effective ness and advanced nature of the optimization model.It has been found that to optimize the SVM model by using the CBBO can accurately and efficiently diagnose any faults of steam turbine rotors.Compared with CPSO (chaos particle swarm optimization) based SVM model,the method in question has an even higher accuracy and identification efficiency to diagnose any faults.