基于多传感器融合声纹特征图谱的变压器铁芯松动故障诊断方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (15) : 129-137.

论文

李楠1，马宏忠1，段大卫1，朱昊1，何萍2

作者信息 +

Fault diagnosis method for transformer core looseness based on multi-sensor fusion voiceprint feature map

LI Nan1, MA Hongzhong1, DUAN Dawei1, ZHU Hao1, HE Ping2

Author information +

文章历史 +

摘要

变压器铁芯轻微松动故障给变压器安全稳定运行留下巨大隐患，目前尚缺乏切实可靠的诊断方法。本文提出一种基于多传感器融合声纹特征图谱的变压器铁芯松动故障诊断方法。首先，利用4个传感器采集声纹时序序列，通过小波变换生成声纹特征图谱，利用熵权法确定不同传感器信号的权重分配，将4个声纹特征图谱加权融合，从而形成多传感器融合声纹特征图谱。其次，将融合声纹特征图谱输入优化后的ShuffleNetV2模型，通过分组卷积和通道混洗得到铁芯松动程度。最后，通过现场实验验证了方法的有效性。结果表明，所提方法对25%、50%、75%及100%的松动程度均能实现可靠诊断，平均准确率高达99.6%。与采用傅里叶频谱(fast Fourier transform,FFT)、格拉米角场(Gramian angular field,GAF)、马尔可夫变换场(Markov transform field,MTF)以及混沌特征(RP)等传统声纹特征图谱的诊断相比，所提方法识别准确率提高了12.2%；与采用单传感器声纹特征图谱的诊断相比，所提方法识别准确度提高了5.8%；与采用AlexNet、MobilleNetV2、GoogleNet以及ResNet等卷积神经网络模型的诊断相比，所提方法识别准确率提高了2.7%。

Abstract

The core looseness fault leaves a huge hidden trouble on the safe and stable operation of the power transformer, a practical and reliable diagnostic method has not yet been presented. In this paper, a multi-sensor-fusion voiceprint based diagnosis method core looseness fault of power transformer is proposed. Firstly, four sensors are used to collect the time-domain series of the voiceprint, which are converted to voiceprints by the wavelet transform. The weight of different voiceprints, which are achieved by different sensors, are calculated by the entropy weight method. By weighted fusion, a multi-sensor-fusion voiceprint is generated. Secondly, the multi-sensor-fusion voiceprint is input into the improved ShuffleNetV2 model, where the fault degree is identified by group convolution and channel shuffling. Finally, the effectiveness of the method is verified experimentally. Experimental results show that the proposed method can reliably diagnose the winding faults with looseness of 25%, 50%, 75% and 100%, and the accuracy rate is up to 99.6%. Compared with conventional voiceprints, such as FFT, GAF, MTF, and RP, the diagnosis accuracy is increased by 12.2%. Compared with single-sensor voiceprint the diagnosis accuracy is increased by 5.8%. Compared with other neural network models, such as AlexNet , MobilleNetV2, GoogleNet and ResNet, the diagnosis accuracy is increased by 2.7%.

导出引用

李楠1，马宏忠1，段大卫1，朱昊1，何萍2. 基于多传感器融合声纹特征图谱的变压器铁芯松动故障诊断方法[J]. 振动与冲击, 2023, 42(15): 129-137

LI Nan1, MA Hongzhong1, DUAN Dawei1, ZHU Hao1, HE Ping2. Fault diagnosis method for transformer core looseness based on multi-sensor fusion voiceprint feature map[J]. Journal of Vibration and Shock, 2023, 42(15): 129-137

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