融合軸電壓-振動(dòng)特征的同步電機(jī)缺陷診斷

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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，F(xiàn)uzhou University，F(xiàn)uzhou 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

同步發(fā)電機(jī)安全運(yùn)行與維持可靠的電力供應(yīng)和電力系統(tǒng)的穩(wěn)定性密不可分，在其運(yùn)行過程中定子回路短路、電壓過高導(dǎo)致變壓器及電機(jī)燒毀、發(fā)電機(jī)變頻器、轉(zhuǎn)子軸承電壓過高等原因會(huì)使同步發(fā)電機(jī)出現(xiàn)故障，嚴(yán)重的可導(dǎo)致同步發(fā)電機(jī)損毀[1]，電機(jī)的常見故障主要可以分為兩大類別：分別是機(jī)械類故障與電氣類故障。（剩余12809字）