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基于Transformer的有载分接开关故障诊断研究

Research on Fault Diagnosis of On-load Tap Changer Based on Transformer
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摘要 针对传统网络捕捉有载分接开关声纹特征之间联系不充分导致故障诊断准确率低的问题,提出了基于Transformer神经网络的有载分接开关故障诊断方法。首先采用梅尔频率倒谱系数提取有载分接开关声纹特征,以降低有载分接开关声纹样本的数据维度。然后利用Transformer充分捕捉声纹特征之间的联系并实现有载分接开关故障诊断。实验结果表明,基于Transformer对有载分接开关传动轴松动、触头磨损、卡涩和连挡故障诊断的准确率高达97.5%,并一定程度缩短了诊断的时间。 In response to the insufficient correlation between traditional network captured acoustic features of On-load tap changer(OLTC),leading to low accuracy in fault diagnosis,this study proposes a fault diagnosis method for OLTC based on Transformer neural network.Firstly,Mel-frequency cepstral coefficients(MFCC)are utilized to extract acoustic features from OLTC sound samples,reducing the data dimensionality of the samples.Subsequently,the Transformer is employed to comprehensively capture the relationships among acoustic features and achieve fault diagnosis for OLTC.Experimental results demonstrate that the Transformerbased approach achieves a high accuracy rate of 97.5%in diagnosing faults such as transmission shaft looseness,contact wear,sticking,and gear engagement issues in OLTC.Moreover,it contributes to a certain extent in shortening the diagnostic time.
作者 宋长铭 李岩 王飞 虞旦旦 SONG Changming;LI Yan;WANG Fei;YU Dandan(School of Energy and Power Engineering,Nanjing University of Science&Technology,Nanjing 210094,China;Kaifeng Vocational College of Culture and Arts,Kaifeng Henan 475001,China)
出处 《自动化与仪器仪表》 2024年第3期26-29,34,共5页 Automation & Instrumentation
关键词 有载分接开关 声纹特征 故障诊断 梅尔频率倒谱系数 TRANSFORMER on-load tap changer acoustic features fault diagnosis mel-frequency cepstral coefficients transformer
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