基于KPCA与模糊积分的燃气轮机状态识别方法

针对舰用燃气轮机结构复杂、工作环境恶劣,难以对其状态进行有效识别问题,提出一种基于核主元分析(kernel principal component analysis,简称KPCA)和模糊积分相结合的状态识别新方法。采用专用试验平台对舰用燃气轮机进行试验,获取其不同工况下的高压转子转速、低压转子转速、涡轮后排气温度及机匣振动等9个状态表征参数的原始信息,采用KPCA方法提取其状态表征参数的不同核主元,构建特征向量空间。并由提取的核主元特征向量分别创建GRNN,Elman神经网络状态识别模型,对燃气轮机状态进行识别。在此基础上,采用模糊积分方法对两种状态识别结果进行决策层融合,得到唯一的状态识别结果,提升了状态识别准确率。研究表明,采用核主元分析和模糊积分相结合的方法,能有效识别出舰用燃气轮机健康与故障状态,具有很好的实际应用价值。

Gas Turbine State Recognition Based on Fuzzy Integral Technology Research

It is difficult to identify the state of a marine gas turbine because of the complicated structure and poor working environment. This paper puts forward a new method combining the kernel principal component analysis (KPCA) and fuzzy integral. First, the KPCA method is adopted to extract nine state characteristics parameters from the kernel principal components, such as the high pressure rotor speed, low pressure rotor speed, turbine exhaust temperature and casing vibration, to create a feature vector space. Then, the nuclear principal eigenvector was created based on the generalized regression neural network (GRNN) for an Elman neural network identification model to identify the gas turbine condition. Finally, the fuzzy integral is used to calculate the gas turbine state according to the result of two kinds of state recognition for policy makers. Research shows that the proposed method can effectively identify the gas turbine health and fault state of the ships by combining the key components, and has very good practical application value.