电力系统保护与控制2024，Vol.52Issue(16)：83-96,14.DOI:10.19783/j.cnki.pspc.240099
基于多维振动特征图谱的特高压换流阀主循环泵轻量化故障诊断模型
A lightweight fault diagnosis model for the main circulating pump of an ultra high voltage converter valve based on a multidimensional vibration feature graph
摘要
Abstract
A lightweight main circulating pump fault diagnosis model based on multidimensional vibration feature graph is proposed to address the difficulties in feature extraction and the large scale of fault diagnosis models in the cooling system of a ultra high voltage converter.First,a time-domain feature extraction method based on vibration locus images(VLI)and pseudo-color coding is proposed to construct the time-domain feature graph of the main pump.Secondly,by integrating Markov transition field(MTF)and wavelet packet transform(WPT),the low frequency and high frequency fault characteristics of vibration signals are extracted at full scale,and the frequency domain and time-frequency domain feature maps of the main pump are constructed.Finally,a lightweight convolutional neural network model framework is improved through omni-dimensional dynamic convolution,and a lightweight main pump fault diagnosis model(OD-ShuffleNet)is constructed.The model integrates time-domain,frequency-domain,and time-frequency domain fault features,further improving the fault diagnosis accuracy while reducing hardware resource consumption.The results show that the diagnostic accuracy of the model is 95.0%,which is better than that achieved by classical convolutional neural network architectures.关键词
特高压换流站/阀冷系统/主循环泵/故障诊断/振动图像Key words
UHV converter station/valve cooling system/main circulating pump/fault diagnosis/vibration image引用本文复制引用
梅飞,张晓光,李剑文,陆嘉华,封通通..基于多维振动特征图谱的特高压换流阀主循环泵轻量化故障诊断模型[J].电力系统保护与控制,2024,52(16):83-96,14.基金项目
This work is supported by the National Key R&D Program of China(No.2022YFE0140600). 国家重点研发计划项目资助(2022YFE0140600) （No.2022YFE0140600）
