融合轴电压-振动特征的同步电机缺陷诊断

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Fault diagnosis of synchronous motors by fusing shaft voltage and vibration features

ZHANG Hang， GUAN Xiangyu， LIAO Jingwen， XU Xinling， CHEN Xiaokun（College of Electrical Engineering and Automation，Fuzhou University，Fuzhou 3501O8，China）

Abstract ：During the operation of synchronous generators， various defects such as rotor eccentricity，turnto-turn short circuits，and static charges may occur， jeopardizing the safe operation of the motor. A method fordiagnosing defects in synchronous generators by integrating shaft voltage-vibration features with deep learning was proposed，based on a nonlinear correlation analysis of shaft voltage signals and mechanical vibration signals under different defects.Firstly，a physical simulation test platform for defects in a three-phase synchronous generator was established to obtain data on shaft voltage and mechanical vibration signals under various operating conditions and defects. The kernel canonical correlation analysis （KCCA）nonlinear correlation analysis algorithm was used to obtain the correlation coeficients between shaft voltage signals and vibration signals. Mel spectrograms were employed for preprocessing the spectrograms of shaft voltage and vibration signals. A paralel double-branch residual neural network （ResNet） was utilized to extract high-dimensional features from both the shaft voltage and vibration spectrograms. Furthermore，a bilinear pooling algorithm was applied to fuse high-dimensional features from diferent modalities，leading to the construction of a clasification model for defects in synchronous generators based on the integration of shaft voltage and vibration features.The results indicates that the correlation between shaft voltage signals and the vibration signals of the synchronous motor exceeded 0.9 in both faulty and normal conditions. The proposed shaft voltage-vibration joint diagnosis model outperforms single shaft voltage and single vibration diagnosis algorithms in terms of accuracy，missed detection rate，and false alarm rateon the test dataset. This work aims to enable timely identification of potential faults and improve the reliability of generator operation by monitoring and analyzing their operational state.

Keywords：shaft voltage；mechanical vibration；correlation analysis；information fusion; fault diagnosis; parallel dual-branch residual network

0引言

同步发电机安全运行与维持可靠的电力供应和电力系统的稳定性密不可分，在其运行过程中定子回路短路、电压过高导致变压器及电机烧毁、发电机变频器、转子轴承电压过高等原因会使同步发电机出现故障，严重的可导致同步发电机损毁[1]，电机的常见故障主要可以分为两大类别：分别是机械类故障与电气类故障。（剩余12809字）