基于深度特征学习的汽轮机转子状态识别方法

State Recognition Method of Turbine Rotor Based on Depth Feature Learning

复杂汽轮机转子振动信号的非平稳性和非线性等问题,会严重影响到汽轮机转子的状态识别。为了保证汽轮机转子的安全运行,提出一种基于对称点模式(symmetrized dot pattern,SDP)特征融合的卷积神经网络(convolutionneural network,CNN)状态识别方法。该方法通过基于SDP分析方法对汽轮机转子各方向、各位置的信号进行特征融合,获取融合特征的SDP图,最终基于CNN进行融合特征SDP图像识别,实现转子故障状态识别。与其他状态识别方法相比,该方法提高了不同状态特征的表征差异,进而提高了学习效果和识别精度。同时,对比实验结果表明,相较于其他状态识别方法,该方法对转子振动状态识别精度最高,达到了96%。

The non-stationary and nonlinearity of vibration signal of complex turbine rotor will seriously affect the state identification of turbine rotor. In order to ensure the safe operation of turbine rotor, a convolutional neural network(CNN) state recognition method based on symmetrized dot pattern(SDP) feature fusion was proposed. Based on the SDP analysis method, the method conducted feature fusion of signals from all directions and positions of the turbine rotor, obtained the SDP diagram of fused characteristics, and finally performed image recognition of fused characteristics SDP based on CNN to realize rotor fault state recognition. Compared with other state recognition methods, this method improved the representation difference of different state characteristics, and then improved the learning effect and recognition accuracy. At the same time, the experimental results show that compared with other state recognition methods, this method has the highest accuracy of rotor vibration state recognition, reaching 96%.