Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments
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摘要: 本文提出一种形状自适应低秩表示的电力设备热故障诊断方法。该方法通过联合超像素分割和低秩表示技术进行热故障诊断。首先，使用主成分分析算法对输入的红外图像进行变换，并对第一主成分进行超像素分割处理，将红外图像自适应地分割为若干非重叠的超像素。然后，采用低秩表示技术对逐个超像素进行热故障诊断，通过充分挖掘空间结构信息和红外温度信息，优化提升热故障诊断精度。实验结果表明，与其他传统热故障诊断方法相比，本文提出的方法在热故障诊断精度上具有较大的优势，满足电力设备红外巡检的应用需求。Abstract: This work introduces a thermal fault diagnosis method that integrates superpixel segmentation and low-rank representation for diagnosis. The proposed method comprises two main steps. First, an input infrared image is transformed using a principal component analysis (PCA) algorithm, and a superpixel segmentation method is employed for the first principal component (PC). The first PC is divided into non-overlapping homogeneous superpixels. Then, the thermal fault region is detected by employing low-rank representation in a superpixel-by-superpixel manner. Experimental results show that the proposed diagnosis method has a better detection performance than that of current state-of-the-art detectors.
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表 1 不同诊断方法的AUC指标
Table 1. AUC values of different diagnosis methods
Test images RX LDP LRR SS-LRR 1 0.9707 0.8312 0.8574 0.9852 2 0.9901 0. 9132 0. 9324 0.9925 3 0.9893 0. 9253 0. 9486 0.9935 
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表 2 不同诊断方法的运行时间
Table 2. Running time of different diagnosis methods
s Test images RX LDP LRR SS-LRR 1 0.59 0.75 0.47 1.66 2 0.53 0.61 0.34 1.52 3 0.84 0.98 0.56 1.81
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