基于锁相平均滤波和矩函数神经网络的压气机气动失稳预警方法OA

Diagnosis and early warning of compressor instability in aero engines are among the current research hot-spots and challenges in the field of aero engines.To address the issues,such as capturing non-integer order fre-quency components in dynamic pressure signals,limited feature dimensionality,complex stall mechanisms,and the difficulty in quantifying evolution trajectories of compressor instability,a compressor aerodynamic instability early warn-ing method is proposed based on phase-locked averaging filter and moment function neural network,using a multi-stage high-speed compressor as the research object.The method first employs phase-locked averaging filter to extract non-integer order frequency disturbance features under high-load conditions.Subsequently,an instability early warn-ing model based on moment function neural network is constructed,which utilizes moment functions to capture global statistical features of instability and local detail features of asymmetric separation and intermittent pulses in early-stage weak signals.Next,Box-Cox transformation is introduced to eliminate heterogeneity among higher-order moment fea-tures,and multi-layer perceptron network layers are adopted to achieve early warning and diagnosis of compressor in-stability.Finally,the effectiveness of the proposed method is validated through test data at different rotational speeds.Results demonstrate that the method accurately characterizes the evolutionary laws of higher-order moment feature spaces,enabling efficient visualization,identification,and separation of instability precursors and steady-state data.Compared with the traditional surge detection method on the compressor rig,it can provide instability warning up to 4.8 s in advance.