Liu Yan, Ma Kai, He Hao, Gao Kuan
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Entropy (Basel). 2020 Apr 9;22(4):424. doi: 10.3390/e22040424.
Based on entropy characteristics, some complex nonlinear dynamics of the dynamic pressure at the outlet of a centrifugal compressor are analyzed, as the centrifugal compressor operates in a stable and unstable state. First, the 800-kW centrifugal compressor is tested to gather the time sequence of dynamic pressure at the outlet by controlling the opening of the anti-surge valve at the outlet, and both the stable and unstable states are tested. Then, multi-scale fuzzy entropy and an improved method are introduced to analyze the gathered time sequence of dynamic pressure. Furthermore, the decomposed signals of dynamic pressure are obtained using ensemble empirical mode decomposition (EEMD), and are decomposed into six intrinsic mode functions and one residual signal, and the intrinsic mode functions with large correlation coefficients in the frequency domain are used to calculate the improved multi-scale fuzzy entropy (IMFE). Finally, the statistical reliability of the method is studied by modifying the original data. After analysis of the relationships between the dynamic pressure and entropy characteristics, some important intrinsic dynamics are captured. The entropy becomes the largest in the stable state, but decreases rapidly with the deepening of the unstable state, and it becomes the smallest in the surge. Compared with multi-scale fuzzy entropy, the curve of the improved method is smoother and could show the change of entropy exactly under different scale factors. For the decomposed signals, the unstable state is captured clearly for higher order intrinsic mode functions and residual signals, while the unstable state is not apparent for lower order intrinsic mode functions. In conclusion, it can be observed that the proposed method can be used to accurately identify the unstable states of a centrifugal compressor in real-time fault diagnosis.
基于熵特征，分析了离心压缩机出口动态压力的一些复杂非线性动力学特性，该离心压缩机在稳定和不稳定状态下运行。首先，对800千瓦的离心压缩机进行测试，通过控制出口防喘振阀的开度来采集出口动态压力的时间序列，同时测试稳定状态和不稳定状态。然后，引入多尺度模糊熵和一种改进方法来分析采集到的动态压力时间序列。此外，利用总体经验模态分解（EEMD）获得动态压力的分解信号，并将其分解为六个本征模态函数和一个残余信号，使用频域中相关系数较大的本征模态函数来计算改进的多尺度模糊熵（IMFE）。最后，通过修改原始数据研究该方法的统计可靠性。通过分析动态压力与熵特征之间的关系，捕捉到了一些重要的内在动力学特性。熵在稳定状态下最大，但随着不稳定状态的加深而迅速减小，在喘振时最小。与多尺度模糊熵相比，改进方法的曲线更平滑，能够准确显示不同尺度因子下熵的变化。对于分解信号，高阶本征模态函数和残余信号能清晰捕捉到不稳定状态，而低阶本征模态函数的不稳定状态不明显。总之，可以看出所提出的方法可用于实时故障诊断中准确识别离心压缩机的不稳定状态。
