摘要
针对汽轮机运行过程中的非平稳性和多分量性振动故障信号,提出一种基于变分模态分解相对熵云模型和优化最小二乘支持向量机(LSSVM)的汽轮机振动故障诊断方法。首先,利用变分模态分解按照预设尺度将故障信号分解为K个模态分量,根据各模态分量与原始信号的相对熵大小去除伪分量,提取最佳分量并将其输入云模型,采用逆向云发生器提取特征向量。然后使用改进果蝇优化算法动态调整搜索步长搜寻影响LSSVM识别精度的超参数最佳组合,最后将特征向量输入参数优化后的LSSVM进行故障识别,并与采用经验模态分解相对熵云模型和集合经验模态分解相对熵云模型的LSSVM识别结果进行了对比。结果表明:所提方法优于传统的信号分解方法,对汽轮机振动故障类别具有很高的识别准确率。
To treat the vibration faults with non-stationary and multi-component features appearing in the running process of steam turbines, a new method for vibration fault diagnosis of steam turbines was proposed based on relative entropy(RE) cloud model of variational mode decomposition(VMD) and optimized least squares support vector machine(LSSVM). Firstly, the fault signal was decomposed into K modal components according to the preset scale by using the variational mode decomposition. The pseudo-components were removed according to the relative entropy of each modal component and the original signal, and subsequently optimal signal components were extracted and put into the cloud model, while the feature vectors were extracted with inverse cloud generator. Then, the improved fruit fly optimization algorithm was used to dynamically adjust the search step to find the best combination of the super-parameters that would affect the identification accuracy of LSSVM. Finally, the LSSVM with optimized input parameters of eigenvectors was used to identify the faults, and the identification results were compared with that of the LSSVM algorithm respectively based on empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) relative entropy cloud model. Results show that the proposed method is superior to traditional signal decomposition methods, which has a high recognition rate for turbine vibration faults.
作者
田松峰
魏言
郁建雄
王傲男
王子光
薛正昂
TIAN Songfeng;WEI Yan;YU Jianxiong;WANG Aonan;WANG Ziguang;XUE Zheng'ang(School of Energy,Mechanical and Power Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China)
出处
《动力工程学报》
CAS
CSCD
北大核心
2019年第10期818-825,共8页
Journal of Chinese Society of Power Engineering
关键词
振动故障
变分模态分解
相对熵
云模型
改进果蝇优化算法
LSSVM
vibration fault
variational mode decomposition
relative entropy
cloud model
improved fruit fly optimization algorithm
LSSVM
