School of Information Science and Technology, Northwest University, Xi'an 710100, China.
Anhui Nanrui Jiyuan Electricity Grid Technical Co., Ltd., Hefei 230088, China.
Sensors (Basel). 2022 Jul 15;22(14):5296. doi: 10.3390/s22145296.
Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.
基于声波的电力故障监测在工业中受到了广泛关注。现有的故障诊断方法通常在现场采集声音信号，并将其传输到后端服务器进行分析，但由于传输数据包丢失和延迟，可能无法提供实时响应。然而，边缘设备的计算能力有限，以及现有的特征提取方法，给边缘诊断带来了重大挑战。在本文中，我们提出了一种快速的电力变压器轻量级故障诊断方法，称为 LightFD，它集成了几个技术组件。首先，在特征提取之前，我们设计了一个非对称汉明余弦窗口函数，以减少信号频谱泄漏并确保数据完整性。其次，我们设计了一种多维时空特征提取方法来提取声学特征。最后，我们设计了一个并行双层双通道轻量级神经网络，以在计算能力有限的边缘设备上实现不同故障类型的分类。广泛的仿真和实验结果表明，LightFD 的诊断精度和召回率分别达到 94.64%和 95.33%，分别比传统的 SVM 方法提高了 4%和 1.6%。
