A Holistic Segmentation Method for Faulty Electrical Equipment under Complex Background
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摘要: 针对变电站电气设备红外监测过程中,获取的红外图像背景复杂而导致故障设备定位不准确、分割难度较大等问题,提出了一种在复杂背景下对故障设备进行定位与整体分割的方法。首先,通过SLIC(Simple Linear Iterative Clustering)超像素算法对图像进行分割,并对超像素块进行Lab颜色空间转换,根据阈值判断是否存在故障并获取故障区域。然后,选取故障图像中最大联通量的较亮点作为种子,利用最大类间方差原理控制种子数目,通过改进区域生长法获取目标主体设备。最后,将故障区域与目标主体设备进行交集运算,完成对故障电气设备的整体分割。研究结果表明,该方法能有效完成复杂背景下的故障电气设备定位与整体分割。与其他分割方法相比,该方法获取的故障电气设备更加完整准确。Abstract: A method of positioning and integral segmentation of faulty equipment in infrared images acquired during the process of infrared monitoring of electrical equipment in substations with complex backgrounds is proposed to contribute to solving problems including inaccurate positioning and difficult segmentation of faulty equipment. First, the image was segmented using the SLIC superpixel algorithm and the superpixel block was transformed into the Lab color space. The faulty area was obtained after the fault was determined based on the threshold value. Second, relatively bright spots with the maximum connectivity in the image, including faulty equipment, were selected as the original seeds. The number of seeds was controlled based on the principle of maximum variance between classes. Accordingly, primary equipment was obtained using an improved regional growth method. Finally, the overall segmentation of the faulty electrical equipment was completed through an intersection calculation between the faulty area and the main equipment. The results show that the positioning and overall segmentation of faulty electrical equipment under complex backgrounds can be successfully completed using the proposed method. Compared with other segmentation methods, identification of faulty electrical equipment using this method is more complete and accurate.
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0. 引言
近年来,太赫兹时域光谱技术在药物检测方面展现出无与伦比的优势。Zhang等[1]获取了不同比例混合的金胺O粉末与蒲黄太赫兹光谱数据,并采用2DCOS-PLSR模型预测样品中金胺O粉末含量。逯美红等[2]以盐酸罂粟碱为研究对象,利用密度泛函理论计算其振动频率,并在此基础上讨论其分子构象和振动模式。周永军等[3]验证了太赫兹光谱在中药材鉴别中的可行性。刘晓庆等人[4]利用太赫兹时域光谱系统获得纯青霉素钠以及来自3个不同厂商的阿莫西林胶囊在0.2~1.4 THz波段的吸收光谱,分析了样品质量与吸收峰的关系。Wang等[5]建立了一种绿色、无损的基于太赫兹指纹峰的膳食补充剂中L-组氨酸和α-乳糖的快速原位分析方法。刘丽萍等[6]将THz-TDS技术与量子化学计算软件Materials Studio相结合,检测并分析了天麻胶囊和天麻素,并对天麻素的吸收峰进行了振动模式分析。Chen[7]的研究结果提示甘草酸是潜在的抗COVID-19的化合物。申美伦等[8]归纳了甘草中甘草酸、甘草次酸的提取和分离纯化方法。丁玲等[9]证实了HPLC(high performance liquid chromatography)法测得甘草中甘草酸、甘草苷含量与利用可见-短波红外技术结合PLS(partial least-square)回归模型预测得到的数据相关性较高。
本文实验测得甘草酸、甘草次酸与甘草苷单质的太赫兹光谱,运用Gaussian09计算甘草酸单分子的太赫兹吸收谱,最后采用一元线性回归模型预测甘草酸浓度。
1. 实验分析
1.1 实验仪器
本实验所用仪器为北京市工业波谱成像工程技术研究中心的透射式THz-TDS平台[10]实验前将干燥的氮气充入密闭的太赫兹光路中,将湿度降低至7%以下才开始实验数据采集,并保证实验进行中样品室及密闭光路系统的湿度始终小于7%,温度保持在约20℃。
1.2 样品制备
甘草酸、甘草苷与甘草次酸性状相似,均为白色粉末。本文实验中均选取纯度大于98%高纯度粉末状样品,其中甘草酸、甘草次酸购买于北京百灵威科技有限公司,甘草苷购置于南京秋实生物科技有限公司,聚乙烯购于Sigma-Aldridge。
根据表 1样品配比,将适量的样品粉末和聚乙烯粉末倒入玛瑙研钵,并混合均匀。然后,将混合后的粉末送入内径13 mm的压片模具,由压片机以6 MPa的压强压制3 min制备成直径约13 mm,厚度约1 mm的圆柱形样片,取出后送入样品干燥柜中备用。按照上述方法,每种样品配置5组,将5组样品的测量数据取平均值,得到最终的太赫兹吸收光谱。
表 1 样品配比信息Table 1. Sample Mixing InformationSample Sample number Powder/mg Pill weight/mg Thickness/mm Sample proportion/% Glycyrrhizic acid (gcs) gcs1 152.7 144.3 1.3 40 gcs2 157.4 152.7 1.4 40 gcs3 162.9 153.2 1.42 40 gcs4 159.6 154.2 1.4 40 gcs5 143.5 122.8 1.1 40 Liquiritin (gcg) gcg1 157.8 152.3 1.3 45 gcg2 159.5 152.8 1.32 45 gcg3 162.6 154.8 1.3 45 gcg4 157.4 149.2 1.28 45 gcg5 134.7 126.6 1.1 45 Glycyrrhetnic acid (gccs) gccs1 155.9 150.6 1.3 45 gccs2 160.0 143.4 1.32 45 gccs3 163.2 157.4 1.4 45 gccs4 160.4 151.8 1.4 45 gccs5 158.9 151.3 1.44 45 1.3 数据处理
首先,分别记录太赫兹光路中的样品信号Esam(t)与参考信号Eref(t)。然后进行傅里叶变换得到对应的频域信号Esam(ω)与Eref(ω),代入吸收系数计算公式(1)、(2),得到样品的太赫兹吸收光谱[11-12]。
$$ {n_{\rm{s}}}(\omega ) = 1 + \frac{c}{{\omega d}}\varphi \left( \omega \right) $$ (1) $$ \alpha \left( \omega \right) = - \frac{2}{d}\ln \left\{ {\frac{{\left| {{E_{{\rm{sam}}}}\left( \omega \right)} \right|}}{{\left| {{E_{{\rm{ref}}}}\left( \omega \right)} \right|}}\frac{{{{\left[ {{n_{\rm{s}}}\left( \omega \right) + 1} \right]}^2}}}{{4{n_{\rm{s}}}\left( \omega \right)}}} \right\} $$ (2) 式中:ω是角频率;c为真空中光速;d为样品厚度;Φ(ω)参考信号与样品信号的相位差,|Esam|、|Eref|分别为样品信号和参考信号的频域幅值。ns(ω)为样品折射率,a(ω)为样品吸收系数。
2. 结果讨论
2.1 实验谱分析
图 1中3种样品的吸收谱线均随频率增加呈不断上升的趋势,且甘草酸、甘草苷及甘草次酸的吸收谱线形状相似,但吸收峰位与强度有明显差别。3种单质的太赫兹吸收峰位参见表 2。
表 2 三种样品吸收峰位Table 2. Peak absorption of three samplesTHz No. 1 2 3 4 5 6 7 8 9 Glycyrrhizic acid (gcs) 1.131 1.440 1.561 1.610 1.655 1.704 - - - Liquiritin (gcg) 0.349 0.433 1.437 1.518 1.564 1.606 1.662 1.714 - Glycyrrhetnic acid (gccs) 0.342 0.427 1.004 1.131 1.44 1.574 1.613 1.662 1.714 观察图 1虚线框内局部放大的吸收特性可知,在0.3~1 THz内甘草酸并无吸收峰,而甘草苷与甘草次酸均存在接近的吸收峰位。1~1.6 THz频段内,可以根据1.004、1.131两个峰位区别甘草苷与甘草次酸。另外,甘草酸、甘草苷及甘草次酸3种物质在多个位置的吸收峰较为接近甚至相同,这是因为三者在化学结构与化学性质上有着很大的相似性。
2.2 甘草酸实验谱与计算谱对比
如图 2所示将甘草酸单分子分为含氧碳环块、碳环块。
首先,采用PM3算法对上述单分子构型进行结构优化与频率计算,得到甘草酸分子的太赫兹吸收计算谱,如图 3黑色虚线所示。观察黑色虚线,甘草酸分子在0.3~1.7 THz范围内3个理论吸收峰分别位于0.869 THz、1.176 THz、1.565 THz处。PM3算法吸收计算谱波形与实验谱波形相差较大,需要改善理论方法,获取更为精确的吸收谱。
密度泛函(Density functional theory)理论中的B3LYP泛函适用于较大体系的单分子结构计算,基组选择6-31G(d),引入色散校正项DFT-D3。另外采用谐振频率校正因子校正分子理论构型与计算方法选择引起的计算频率与实验数据之间的偏差。在CCCBDB查得6-31G(d)基组的校正因子为0.96。最终计算谱如图 3蓝色点划线所示。观察蓝色点划线发现,PM3理论计算值1.565THz与实验值1.561 THz吻合,但是1.561 THz吸收强度的实验值相对较弱。计算谱中1.176 THz的理论计算值接近实验光谱中1.131 THz的峰值位置。基于DFT计算的吸收光谱的峰值位于1.279 THz和1.661 THz,并且波形与实验谱更加一致,而理论计算值1.661 THz与实验值1.655 THz符合,证明理论方法的选择是合理的。
2.3 一元线性回归模型预测
朗伯比尔定律是光吸收基本定律,其表达形式为:
$$ A = \varepsilon *d*c $$ (3) 式中:A为样片吸收系数;ε为单位摩尔吸收系数;d为样片厚度;c为样片浓度。本节制备不同质量分数的甘草酸样片,样片信息如表 3所示,利用太赫兹时域光谱系统获取太赫兹吸收光谱。观察图 4发现3种甘草酸太赫兹吸收谱的基线斜率会随浓度的增大而上升。为了验证甘草酸太赫兹吸收系数与浓度之间的线性关系,选取特征吸收峰1.655 THz及其附近6个数值点的太赫兹吸收系数如表 4所示,取其平均值与浓度进行一元线性回归拟合。结果如图 5所示。从图中可以看出甘草酸太赫兹吸收光谱符合朗伯比尔定律。一元线性回归模型为:y=93.74173x-18.56105,相关系数R2=0.99824。利用一元线性回归模型预测样品的浓度,结果见表 5。
表 3 不同浓度甘草酸样品的配比信息Table 3. Proportion information of glycyrrhizic acid samples with different concentrationsSample number Powder/mg Pill weight/mg Thickness/mm Sample proportion/% Concentration/(mol/L) gcs01 158.4 155.7 1.28 20 0.312 gcs02 161.2 158.7 1.43 30 0.426 gcs03 159.6 154.2 1.4 40 0.574 表 4 1.655 THz及其附近6个频率点的吸收系数值Table 4. Absorption coefficient values at 1.655 THz and 6 frequency points around itFrequency/THz Absorption/(gcs01) Absorption/ (gcs02) Absorption/ (gcs03) 1.646 10.935 19.566 34.327 1.649 11.026 20.573 35.255 1.652 11.074 21.274 36.572 1.655 11.083 21.473 38.239 1.659 11.061 21.276 36.482 1.662 11.017 20.876 34.127 1.665 10.962 20.404 33.539 Average absorption 11.023 20.777 35.506 表 5 浓度预测值及相对误差Table 5. Concentration prediction values and relative errorsSample number gcs01 gcs02 gcs03 Prediction/(mol/L) 0.316 0.420 0.577 Real/(mol/L) 0.312 0.426 0.574 Relative error/% 1.28 1.41 0.52 3. 结论
本文首先制备了甘草酸、甘草次酸以及甘草苷样片,利用透射式太赫兹时域光谱系统测得上述样片的太赫兹光谱,发现它们的谱线相似。其次,构建甘草酸单分子构型,并利用Gaussian09软件对其进行了结构优化与频率计算,获得了太赫兹计算谱。对比发现,随着理论方法的改进,甘草酸的太赫兹吸收计算谱和实验谱不仅在峰位上对应,且太赫兹吸收谱波形也趋于一致。最后制备含量分别为20%,30%,40%的甘草酸样品,通过一元线性回归模型拟合了甘草酸太赫兹光谱吸收系数与浓度的关系,验证了甘草酸的太赫兹吸收光谱符合朗伯比尔定律。
致谢:感谢北京市工业波谱成像工程技术研究中心提供的太赫兹时域光谱实验平台,感谢北京科技大学自动化学院的于洋博士在实验方面给予的帮助和有益讨论。
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表 1 4种算法的实验结果
Table 1 Experimental results due to four algorithms
Segmentation method OTSU Marked watershed Region growing Ours Precision Recall Precision Recall Precision Recall Precision Recall Picture1 0.4259 0.9271 0.3190 0.4037 0.9360 0.7686 0.9596 0.9240 Picture2 0.8438 0.8425 0.8060 0.9148 0.7244 0.8680 0.9324 0.9660 Picture3 0.4325 0.8945 0.4327 0.9192 0.7109 0.8136 0.9394 0.9393 Picture4 0.4924 0.9582 0.5474 0.9306 0.7032 0.9454 0.9485 0.9218 Picture5 0.2527 0.9322 0.4624 0.9261 0.6862 0.9244 0.9682 0.9388 -
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