Article(id=1304921738056331461, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1304921635748864029, articleNumber=null, orderNo=null, doi=10.16790/j.cnki.1009-9239.im.2026.07.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751904000000, receivedDateStr=2025-07-08, revisedDate=1758038400000, revisedDateStr=2025-09-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1789047186626, onlineDateStr=2026-09-10, pubDate=1784476800000, pubDateStr=2026-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789047186626, onlineIssueDateStr=2026-09-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789047186626, creator=13701087609, updateTime=1789047186626, updator=13701087609, issue=Issue{id=1304921635748864029, tenantId=1146029695717560320, journalId=1149653034449285133, year='2026', volume='59', issue='7', pageStart='1', pageEnd='160', issueExtLink='null', onlineDate='null', pubDate='1784476800000', pubDateStr='2026-07-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789047162234, creator='13701087609', updateTime=1789117876219, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305218231761920521, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1304921635748864029, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305218231761920522, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1304921635748864029, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=152, endPage=160, ext={EN=ArticleExt(id=1304921738542870726, articleId=1304921738056331461, tenantId=1146029695717560320, journalId=1149653034449285133, language=EN, title=Rapid detection of mechanical properties for cross-linked polyethylene cable insulation based on near-infrared spectroscopy, columnId=1192878364340924664, journalTitle=Insulating Materials, columnName=Test and Analysis, runingTitle=null, highlight=null, articleAbstract=

This study aimed to establish a rapid on-site detection method for the mechanical properties of cross-linked polyethylene (XLPE) cable insulation based on near-infrared spectroscopy. Considering the high light transmittance of XLPE insulation and taking into account spectral repeatability and signal-to-noise ratio, a transflective "6+1" fiber optic acquisition probe and a supporting detection device were developed. The near-infrared spectra of 60 cable samples were collected, and the influence of spectral baseline drift was eliminated by second-order derivative pretreatment. The elongation at break and tensile strength of the samples were measured according to GB/T 2951.11—2008 and used as reference values for modeling, and the repeatability of the reference data was verified. The preprocessed spectral matrix was correlated with the reference values using partial least squares (PLS) regression, and a multivariate calibration model for the mechanical properties of XLPE insulation was established. The results show that the cross-validation standard errors of the model for elongation at break and tensile strength are 12.16 and 0.55, respectively. When five blind samples were prepared by the model, the relative deviations between the predicted results and the reference values are all less than 5%, demonstrating that the model can effectively achieve rapid assessment of the mechanical properties of XLPE insulation.

, authors=Kaiwen Huang1, 2, Chenhui Liu3, Benhong Ouyang1, 2, *, Yuli Wang1, 2, Jianjun Yuan1, 2, Zhen Yuan3, Songhua Liu1, 2, authorsList=Kaiwen Huang, Chenhui Liu, Benhong Ouyang, Yuli Wang, Jianjun Yuan, Zhen Yuan, Songhua Liu, authorCompany=null, correspAuthors=Benhong Ouyang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1304921742976250075, articleId=1304921738056331461, tenantId=1146029695717560320, journalId=1149653034449285133, language=CN, title=基于近红外光谱特征的交联聚乙烯电缆绝缘力学性能快速检测研究, columnId=1192878364483531003, journalTitle=绝缘材料, columnName=测试与分析, runingTitle=null, highlight=null, articleAbstract=

本研究基于近红外光谱技术建立一种适用于电缆交联聚乙烯(XLPE)绝缘层力学性能指标的现场快速检测方法。针对XLPE绝缘层高透光性的特点,综合考量光谱重复性与信噪比,搭建了一种透反射式“6+1”型光纤采集探头及配套检测装置。采集了60个电缆样品的近红外光谱,并采用二阶微分预处理消除了光谱基线漂移的影响。参照GB/T 2951.11—2008测定了样品的断裂伸长率和抗张强度,将其作为建模参考值,验证参考值数据的重复性。采用偏最小二乘法将预处理后的光谱矩阵与参考值进行关联,建立了XLPE绝缘层力学性能指标的多元校正模型。结果表明:该模型对断裂伸长率和抗张强度的交叉验证标准误差分别为12.16和0.55;利用模型对5个盲样进行预测,预测结果与参考值的相对偏差均小于5%,证明该模型能够有效实现XLPE绝缘层力学性能的快速评估。

, authors=黄凯文1, 2, 刘晨晖3, 欧阳本红1, 2, *, 王昱力1, 2, 袁建军1, 2, 袁振3, 刘松华1, 2, authorsList=黄凯文, 刘晨晖, 欧阳本红, 王昱力, 袁建军, 袁振, 刘松华, authorCompany=null, correspAuthors=欧阳本红, authorNote=

黄凯文(1994-),男(汉族),湖北黄石人,工程师,主要从事电力电缆系统性能可靠性评价及高端电缆装备研发工作

, correspAuthorsNote=
欧阳本红(1980-),男(汉族),湖北孝感人,教授级高级工程师,主要从事电力电缆结构设计、性能提升及运行维护技术的研究。
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黄凯文(1994-),男(汉族),湖北黄石人,工程师,主要从事电力电缆系统性能可靠性评价及高端电缆装备研发工作

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黄凯文(1994-),男(汉族),湖北黄石人,工程师,主要从事电力电缆系统性能可靠性评价及高端电缆装备研发工作

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figureFileBig=rThSGaazzoqzTnpuifSdqw==, tableContent=null), ArticleFig(id=1304921749825548588, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=EN, label=Table1, caption=

Mechanical properties parameters of sixty samples

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统计参数断裂伸长率/%抗张强度/MPa
分布范围405.07~740.5323.11~28.51
平均值489.0525.56
标准差78.831.17
变异系数16.12%4.56%
), ArticleFig(id=1304921749905240365, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=CN, label=表1, caption=

60个样品的力学性能参数

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统计参数断裂伸长率/%抗张强度/MPa
分布范围405.07~740.5323.11~28.51
平均值489.0525.56
标准差78.831.17
变异系数16.12%4.56%
), ArticleFig(id=1304921749976543534, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=EN, label=Table 2, caption=

Statistical parameters of the calibration model prediction

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项目校正集样品数最大偏差最大相对偏差/%R2SCV
断裂伸长率6027.425.680.9912.16
抗张强度601.013.960.890.55
), ArticleFig(id=1304921750052041007, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=CN, label=表2, caption=

校正模型预测结果的统计参数

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项目校正集样品数最大偏差最大相对偏差/%R2SCV
断裂伸长率6027.425.680.9912.16
抗张强度601.013.960.890.55
), ArticleFig(id=1304921750127538480, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=EN, label=Table 3, caption=

Comparison between predicted and reference values of elongation at break of blind samples

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编号参考值/%预测值/%绝对偏差/%相对偏差/%
1#410.25426.3516.13.92
2#485.12478.63-6.49-1.34
3#505.78512.546.761.34
4#645.12634.1-11.02-1.71
5#732.38718.19-14.19-1.94
), ArticleFig(id=1304921750207230257, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=CN, label=表3, caption=

盲样断裂伸长率预测值与参考值对比

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编号参考值/%预测值/%绝对偏差/%相对偏差/%
1#410.25426.3516.13.92
2#485.12478.63-6.49-1.34
3#505.78512.546.761.34
4#645.12634.1-11.02-1.71
5#732.38718.19-14.19-1.94
), ArticleFig(id=1304921750274339122, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=EN, label=Table 4, caption=

Comparison between predicted and reference values of tensile strength of blind samples

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编号参考值/MPa预测值/MPa绝对偏差/MPa相对偏差/%
1#23.5624.120.562.38
2#24.8324.37-0.46-1.85
3#25.8425.28-0.56-2.17
4#26.1726.320.150.57
5#27.2527.890.642.35
), ArticleFig(id=1304921750354030899, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=CN, label=表4, caption=

盲样抗张强度预测值与参考值的对比

, figureFileSmall=null, figureFileBig=null, tableContent=
编号参考值/MPa预测值/MPa绝对偏差/MPa相对偏差/%
1#23.5624.120.562.38
2#24.8324.37-0.46-1.85
3#25.8425.28-0.56-2.17
4#26.1726.320.150.57
5#27.2527.890.642.35
), ArticleFig(id=1304921750429528372, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=EN, label=Table 5, caption=

Repeatability test results of mechanical properties for representative samples

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编号指标第一次第二次第三次参考值重复性标准偏差相对标准偏差
30#断裂伸长率505.75498.62496.67500.354.783.22%
抗张强度26.6226.6426.7826.680.090.74%
34#断裂伸长率441.24443.87443.34442.81.391.59%
抗张强度25.2025.2725.1725.210.050.87%
40#断裂伸长率623.28620.24632.84625.466.573.03%
抗张强度25.5425.6525.4825.560.090.21%
), ArticleFig(id=1304921750505025845, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1304921738056331461, language=CN, label=表5, caption=

典型代表性样品的力学性能重复性实验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
编号指标第一次第二次第三次参考值重复性标准偏差相对标准偏差
30#断裂伸长率505.75498.62496.67500.354.783.22%
抗张强度26.6226.6426.7826.680.090.74%
34#断裂伸长率441.24443.87443.34442.81.391.59%
抗张强度25.2025.2725.1725.210.050.87%
40#断裂伸长率623.28620.24632.84625.466.573.03%
抗张强度25.5425.6525.4825.560.090.21%
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基于近红外光谱特征的交联聚乙烯电缆绝缘力学性能快速检测研究
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黄凯文 1, 2 , 刘晨晖 3 , 欧阳本红 1, 2, * , 王昱力 1, 2 , 袁建军 1, 2 , 袁振 3 , 刘松华 1, 2
绝缘材料 | 测试与分析 2026,59(7): 152-160
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绝缘材料 |测试与分析 2026 , 59 (7) : 152 -160
基于近红外光谱特征的交联聚乙烯电缆绝缘力学性能快速检测研究
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黄凯文(1994-),男(汉族),湖北黄石人,工程师,主要从事电力电缆系统性能可靠性评价及高端电缆装备研发工作

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黄凯文(1994-),男(汉族),湖北黄石人,工程师,主要从事电力电缆系统性能可靠性评价及高端电缆装备研发工作

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黄凯文1, 2, 刘晨晖3, 欧阳本红1, 2, *, 王昱力1, 2, 袁建军1, 2, 袁振3, 刘松华1, 2
作者信息
  • 1中国电力科学研究院有限公司,湖北 武汉 430074
  • 2电网环境保护全国重点实验室,湖北 武汉 430074
  • 3中电保力(北京)科技有限公司,北京 100020
通讯作者:
欧阳本红(1980-),男(汉族),湖北孝感人,教授级高级工程师,主要从事电力电缆结构设计、性能提升及运行维护技术的研究。
作者简介:

黄凯文(1994-),男(汉族),湖北黄石人,工程师,主要从事电力电缆系统性能可靠性评价及高端电缆装备研发工作

Rapid detection of mechanical properties for cross-linked polyethylene cable insulation based on near-infrared spectroscopy
Kaiwen Huang1, 2, Chenhui Liu3, Benhong Ouyang1, 2, *, Yuli Wang1, 2, Jianjun Yuan1, 2, Zhen Yuan3, Songhua Liu1, 2
Affiliations
  • 1China Electric Power Research Institute Co., Ltd., Wuhan 430074, China
  • 2State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China
  • 3Polypower (Beijing) Technology Co., Ltd., Beijing 100020, China
出版时间: 2026-07-20 doi: 10.16790/j.cnki.1009-9239.im.2026.07.017
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本研究基于近红外光谱技术建立一种适用于电缆交联聚乙烯(XLPE)绝缘层力学性能指标的现场快速检测方法。针对XLPE绝缘层高透光性的特点,综合考量光谱重复性与信噪比,搭建了一种透反射式“6+1”型光纤采集探头及配套检测装置。采集了60个电缆样品的近红外光谱,并采用二阶微分预处理消除了光谱基线漂移的影响。参照GB/T 2951.11—2008测定了样品的断裂伸长率和抗张强度,将其作为建模参考值,验证参考值数据的重复性。采用偏最小二乘法将预处理后的光谱矩阵与参考值进行关联,建立了XLPE绝缘层力学性能指标的多元校正模型。结果表明:该模型对断裂伸长率和抗张强度的交叉验证标准误差分别为12.16和0.55;利用模型对5个盲样进行预测,预测结果与参考值的相对偏差均小于5%,证明该模型能够有效实现XLPE绝缘层力学性能的快速评估。

电缆  /  交联聚乙烯  /  力学性能  /  近红外光谱  /  快速评估

This study aimed to establish a rapid on-site detection method for the mechanical properties of cross-linked polyethylene (XLPE) cable insulation based on near-infrared spectroscopy. Considering the high light transmittance of XLPE insulation and taking into account spectral repeatability and signal-to-noise ratio, a transflective "6+1" fiber optic acquisition probe and a supporting detection device were developed. The near-infrared spectra of 60 cable samples were collected, and the influence of spectral baseline drift was eliminated by second-order derivative pretreatment. The elongation at break and tensile strength of the samples were measured according to GB/T 2951.11—2008 and used as reference values for modeling, and the repeatability of the reference data was verified. The preprocessed spectral matrix was correlated with the reference values using partial least squares (PLS) regression, and a multivariate calibration model for the mechanical properties of XLPE insulation was established. The results show that the cross-validation standard errors of the model for elongation at break and tensile strength are 12.16 and 0.55, respectively. When five blind samples were prepared by the model, the relative deviations between the predicted results and the reference values are all less than 5%, demonstrating that the model can effectively achieve rapid assessment of the mechanical properties of XLPE insulation.

cable  /  cross-linked polyethylene (XLPE)  /  mechanical property  /  near-infrared spectroscopy  /  rapid assessment
黄凯文, 刘晨晖, 欧阳本红, 王昱力, 袁建军, 袁振, 刘松华. 基于近红外光谱特征的交联聚乙烯电缆绝缘力学性能快速检测研究. 绝缘材料, 2026 , 59 (7) : 152 -160 . DOI: 10.16790/j.cnki.1009-9239.im.2026.07.017
Kaiwen Huang, Chenhui Liu, Benhong Ouyang, Yuli Wang, Jianjun Yuan, Zhen Yuan, Songhua Liu. Rapid detection of mechanical properties for cross-linked polyethylene cable insulation based on near-infrared spectroscopy[J]. Insulating Materials, 2026 , 59 (7) : 152 -160 . DOI: 10.16790/j.cnki.1009-9239.im.2026.07.017
截至2024年底,国家电网在运输配电电缆线路已超过100万公里,电缆已成为城市电能输送的“主动脉”[1]。随着城市化进程推进,我国电力电缆规模呈爆发式增长[2],提升电缆设备本质安全和质量管控水平在保障电网运行安全、运检业务成效方面具有显著意义。
当前,电线电缆产品抽查检验项目有20℃导体电阻检验、绝缘厚度检验、护套厚度检验、绝缘老化前后抗张强度和断裂伸长率检验、护套老化前后抗张强度和断裂伸长率检验等。其中,绝缘材料的力学性能测试一般是参照GB/T 2951.11—2008对老化7天前后的哑铃型样片进行试验。但是该方法存在抽检覆盖率低、试验耗时长、取样破坏性强等问题,亟需一种使用便捷、准确率高的新型检测分析手段。
近红外光谱(NIR)产生于分子振动的倍频和组合频吸收,可从分子水平上反映物质的组成与结构信息。当物质的组成确定时,其近红外光谱也随之确定,具有唯一性,故被称为分子指纹光谱[3]。现代科学仪器技术的发展已实现样品近红外光谱的快速和无损采集。结合近红外光谱和化学计量学,可实现物质多种属性的检测,已经广泛用于石化、制药、农业等众多领域[4-5],成为现场或在线检测不可或缺的分析手段。近红外光谱在高聚物材料性能测试中的研究和应用也有报道,袁洪福等[6]使用近红外光谱建立了聚异丁烯高活性端基含量及相对分子质量的测定方法。张彦君等[7]建立了聚丙烯粒料和粉料的在线近红外分析系统,实现了聚丙烯树脂熔融指数、等规度、堆积密度和乙烯含量指标的同时测定。谢锦春等[8]通过最小二乘方法将乙烯醋酸乙烯酯共聚物(EVA)的近红外光谱与断裂伸长率进行关联,建立了EVA断裂伸长率的定量分析模型。此外,他们还利用近红外光谱与族类独立软模式方法(SIMCA)对不同牌号的乙烯醋酸乙烯酯(EVA)共聚物树脂进行快速定性分析[9]。雷玉等[10]采用近红外光谱对聚合物共混挤出工艺质量控制方面进行了研究。M Mizushima等[11]使用原位近红外光谱研究了聚乙烯在熔融状态时的结构变化。H Shinzawa等[12]使用近红外光谱对聚丙烯的老化进行了研究。综上所述,近红外光谱检测的聚合物性能指标主要包括共聚物组成、熔融指数、等规度、堆积密度和分子量等,所测的样品主要为生产工艺中的粒料和粉料样品。在XLPE绝缘材料中,甲基(-CH3)、亚甲基(-CH2-)等含氢基团的近红外吸收峰与分子链结构密切相关[13]。材料中的结构变化都会导致近红外光谱的变化,尽管这种变化非常细微,但通过化学计量学方法对光谱数据进行处理,便能得到样品组成变化的信息。
理论上,近红外光谱能反映XLPE材料的组成信息,因此利用近红外光谱测试XLPE力学性能指标成为可能[14]。实际上,对于包裹在电缆中的XLPE绝缘层,使用通用型近红外光谱仪无法直接采集其光谱。不同电压等级电缆的绝缘层厚度不同,同时外包裹层中的半导电屏蔽层,均可能会影响测试光束在样品中的传播路径,从而影响XLPE绝缘光谱的采集。已有的光谱采集方式并不适用于电缆中XLPE的光谱采集,获取满足分析精度要求的XLPE绝缘层的近红外光谱是一项高难度的技术挑战。此外,聚合物近红外光谱响应与其力学性能参数间存在显著的非线性映射关系[15-18],难以构建高精度的光-力学性能关联数学模型,大幅提升了试样力学性能定量预测的难度。因此,研究近红外光谱检测电缆中XLPE绝缘层的力学性能指标的高效快速方法,具有重要的学术意义和应用价值。
本文收集了多种常用的不同型号电缆样品,比较漫反射和透反射两种采集方式,在实验室分析不同方法下近红外光谱的一致性,以构建合适的光谱采集方法,设计并搭建用于电缆绝缘层光谱采集的仪器。结合近红外光谱和多元分析方法,提出一种快速、可同时检测电缆中XLPE绝缘层断裂伸长率和抗张强度的分析方法,以期实现电缆中XLPE绝缘层力学性能指标的快速同时检测,为XLPE电缆力学性能的现场测试提供重要技术支撑。
本文使用的电缆样品来自国内20余个电缆厂家生产的10 kV电力电缆,从成盘电缆尾端取样,样品长度为100 cm。总共收集了65个样品,其中60个样品用于数据库的建立,5个样品用于盲样预测。
近红外光谱(NIR)在电磁波谱图上介于可见光与中红外光谱之间,其光谱范围为780~2 500 nm。当近红外光照射到固体样品表面时,会产生光的镜面反射、漫反射、散射、吸收和透射[13],如图1所示。近红外光谱检测固态样品的方式主要有漫反射和透反射两种方式[18]。现阶段商用的仪器主要是对进入材料浅表层并携带样品信息反射出来的漫反射光进行检测,这是非接触式或原位检测的一种有效方式,不具有破坏性。但是,对于表面光滑或具有高透光性的样品以及低浓度组分的检测,漫反射信号弱,透反射为更有效的检测方式[19-21]。因此,需要充分考虑样品的表面形态和透光度等特性,较高的信噪比和良好的重复性是选择检测方式时最为重要的两个指标。
本文首先评估漫反射和透反射两种光谱采集方式的光谱质量。沿电缆内芯轴向去除外半导电屏蔽层结构,完全暴露出绝缘层,并基于此搭建一套原位漫反射检测装置,用于电缆绝缘层光谱采集,如图2所示。漫反射检测装置由汇聚卤钨灯、高聚焦透镜、光栅分光数字微镜阵列光谱模块(NIR-M-R3型,Inno-Spec公司)和蓝宝石检测窗口组成。透反射检测装置是在漫反射装置的基础上变更采样方式,在样片上部加盖一个高纯聚四氟乙烯反射板,在制备样品时,将绝缘层沿电缆段轴向平切1 mm厚的绝缘层薄片,如图3所示。样品检测方式保持一致,具体如下:每个样品采集3个采样点,每个采样点采集3次光谱,取平均光谱作为每个采样点的光谱。
为比较漫反射与透反射两种采集方式对XLPE绝缘层近红外光谱获取效果的影响,选取同一电缆样品,在其绝缘层表面设置不同采样点,分别采用两种方式采集近红外光谱,用于评价两种采集方式下光谱曲线的重复性、平滑程度及特征吸收峰的一致性,结果如图4所示。由图4可以看出,相比于漫反射检测方式,透反射检测获取的光谱曲线更加光滑,说明噪声信号弱。两种检测方式获取的光谱曲线均有明显的特征吸收峰,其中1 360~1 390 nm和1 410~1 450 nm谱带对应不同C-H基团的伸缩振动和弯曲振动的合频峰;1 660~1 800 nm谱带对应不同C-H基团伸缩振动的倍频峰[13]。从图4还可以看出,透反射检测方式采集的3次光谱曲线一致性显著优于漫反射检测方式,这有利于后期模型建立。
在上述研究的基础上,结合电缆结构特点和电缆取样的需求,本研究设计了基于透反射模式的“6+1”光纤采集探头,如图5所示。
在上述光谱采集方式对比和探头结构设计的基础上,本文进一步集成双光路光源、Y型光纤传输、阵列检测型光谱单元和光栅分光模块,形成面向XLPE绝缘层快速检测的近红外光谱仪样机。该样机通过入射光与收集光的同轴化传输、样品夹持定位及一体化封装设计,提高了光路结构的紧凑性和现场操作的便利性;其光谱采集重复性和信号稳定性将在后续重复性测试结果中得到进一步验证。结合机械结构、光学系统、探测器系统和光路设计及集成封装的便携式光谱仪样机整体结构如图6所示。
便携式光谱仪样机型号为NIR-EPRI,波长范围为1 350~2 150 nm,光谱分辨率为4 nm,采样时间小于1 min,光斑直径为10 mm,质量为12 kg,尺寸为30 cm×40 cm×50 cm,待机时间为6 h,可覆盖单日50个点位检测。GB/T 2951.11—2008中力学性能测试需要经历取样、制片、哑铃型试样制备及拉伸测试等步骤,若涉及老化后力学性能评价,还需完成规定条件下的热老化处理,整体测试周期通常以小时至天计。相比之下,本文方法在完成样品制备后,单样品光谱采集与模型预测耗时不超过5 min,可显著缩短XLPE绝缘层力学性能快速筛查时间。下文所有光谱采集数据均使用此研发设备采集而得。
使用合适工具去除电缆的外护套、铠装层和防水层,取出电缆绝缘线芯,切取长度为70 cm的样段,并分切长度为20 cm和50 cm的两部分,分别用于光谱采集和力学性能检测,保证两者来源一致。光谱采集制样:使用交联电缆切片机(JQB-III型,嘉兴市凯博实验仪器有限公司)转动旋钮调整单芯电缆上表面与刀片间距,切割单层XLPE样品,样品长度为20 mm、宽度为10 mm、厚度为1 mm。力学性能检测制样:依据GB/T 2951.11—2008,制备用于测试断裂伸长率和抗张强度指标的哑铃型样品。
在室温下,光谱仪开机后预热30 min,将制备的样片放入光纤探头夹内,使用支架固定住探头,静置30 s,采集其漫反射光谱。设置波长范围为1 350~2 150 nm,分辨率为5 nm,扫描次数为25次。样片等距取3点,每点采集3张光谱,取其平均光谱为样品光谱,同时保留每次采集的光谱图。
力学性能指标包括断裂伸长率、抗张强度。按照GB/T 2951.11—2008制样,得到5个哑铃型样片,每种指标测量5个数值,取其平均值作为指标值,并计算其相对标准偏差(RSD)和变异系数(标准偏差与指标值的比值)。
对近红外光谱、力学性能测试结果及模型预测结果进行绘图分析。对每个XLPE绝缘样品的断裂伸长率和抗张强度测试结果进行统计,计算其平均值、标准差和相对标准偏差,用于评价力学性能参考值的重复性。对原始近红外光谱进行二阶微分预处理,以削弱基线漂移和光散射差异对光谱分析的影响,并提高重叠谱带的分辨能力。随后,采用偏最小二乘回归法(PLSR)建立二阶微分光谱与XLPE绝缘层力学性能指标之间的定量校正模型,并分别采用交叉验证和外部验证评价模型预测性能。
采用留一法交叉验证对校正模型进行评价,其交叉验证预测标准误差(SCV)按式(1)计算。
SCV=1nci=1nc(yc,i-y^cv,i)2
式(1)中:yc,i为校正集中第i个样品的参考值;y^cv,i为留一法交叉验证过程中第i个样品的模型预测值;nc为校正集样品数。
外部验证集的预测标准误差(SP)按式(2)计算。
SP=1nvi=1nv(yv,i-y^v,i)2
式(2)中:yv,i为验证集中第i个样品的参考值;y^v,i为验证集中第i个样品的模型预测值;nv为验证集样品数。
图7为5个不同样品的光谱标准差曲线。从图7可以看出,在所有波长处的标准差均小于0.001 3,说明本仪器测试的近红外光谱具有较好的重复性。
60个样品的力学性能参数如表1所示。从表1可以看出,不同电缆样品之间的断裂伸长率存在明显差异,样品集具有较宽的性能分布范围,有利于后续近红外定量校正模型的建立;抗张强度整体离散程度低于断裂伸长率,表明不同样品的抗张强度差异相对较小。总体来看,所选样品能够覆盖一定范围内的XLPE绝缘力学性能变化,可为建立光谱信息与力学性能指标之间的关联模型提供数据基础。
一般通过近红外光谱模型的交叉验证标准误差(SCV)或预测标准误差(SP)与参考方法的再现性进行比较,评价所建近红外模型的预测结果准确性[19-21]。如果二者不大于参考方法的再现性,则认为近红外分析方法结果与参考方法结果一致。由于XLPE绝缘层力学性能测试结果会受到试样制备、样片厚度均匀性、夹持状态以及材料局部不均一性等因素影响,同一样品的多次力学测试结果通常会表现出一定的离散性。为评价模型参考值的稳定性,本文按照GB/T 2951.11—2008制备并测试哑铃型样品,计算各样品断裂伸长率和抗张强度测试结果的标准差,结果如图8所示。从图8可以看出,60个样品断裂伸长率标准差的中位数为15.14%,抗张强度标准差的中位数为1.15%,表明参考方法获得的力学性能数据具有一定离散性,但整体处于可用于定量建模的范围内,因此将按GB/T 2951.11—2008测得的力学性能数据作为定标建模的参考值。
图9为本研究采集的60个XLPE绝缘样品的原始近红外光谱及其二阶微分光谱,其中每条曲线对应一个XLPE绝缘样品。从图9(a)可以看出,不同样品的原始近红外光谱在整体轮廓上基本相似,说明各样品主要化学基团组成基本一致;但不同曲线之间存在整体吸光度水平及谱线倾斜程度的差异,表现出一定的基线漂移现象。该现象主要与样品厚度、样品表面状态以及光散射效应有关,会影响不同样品光谱细微结构的直接比较结果。
XLPE近红外吸收主要来源于-CH3、-CH2-等含氢基团振动的倍频和合频吸收。由于近红外谱带通常较宽,不同化学基团的吸收峰存在明显重叠,原始光谱中难以直接观察到用于基团精细识别的尖锐特征峰。从图9(b)可以看出,经过二阶微分处理后,光谱峰形更加清晰,重叠谱带的分辨率得到提高,基线漂移的影响被削弱。二阶微分光谱中的峰-谷幅值与相应官能团吸收强度变化有关,因此能够更敏感地反映-CH2-、-CH3等基团含量及其微观结构差异。
1 360~1 390 nm和1 410~1 450 nm谱带主要为-CH2-伸缩振动倍频与其弯曲振动倍频的组合频;1 660~1 800 nm谱带为-CH2-和-CH3伸缩振动的合频峰[11]。从图9(b)还可以看出,不同样品各谱带特征峰峰位相同,但其强度存在差异,表明它们具有相同的化学基团,但各基团含量有所不同。XLPE力学性能与结晶度、片晶厚度、交联度等密切关联[22]。结晶度、片晶厚度对应的长螺旋结构含量,与分子量对应的端甲基含量,以及交联度对应的叔碳、季碳原子个数等结构参数均能通过-CH2-、-CH₃的吸收峰反映[13]。可通过化学计量学方法将光谱中的官能团吸收强度和形貌差异与宏观力学性能建立相关性。
使用Kennard-Stone(KS)样本划分方法[19]将65个样品分为校正集(60个样品)和验证集(5个样品)。对校正集的近红外光谱数据进行二阶微分处理(窗口7,次数2),使用PLS算法,将其二阶微分光谱数据分别与其断裂伸长率和抗张强度数据进行关联;使用留一法[23-24]进行交互验证,建立这两个指标的多元校正模型,校正模型预测结果的统计参数如表2所示,包括最大偏差、最大相对偏差、决定系数R2和交叉验证预测标准误差SCV。从表2可以看出,断裂伸长率模型的R2为0.99,SCV为12.16,最大相对偏差为5.68%;抗张强度模型的R2为0.89,SCV为0.55,最大相对偏差为3.96%。结果表明,基于二阶微分近红外光谱建立的PLSR模型能够较好地预测XLPE绝缘层的力学性能指标。
为进一步直观评价模型预测值与参考值之间的一致性,将60个样品经留一法交叉验证得到的预测值与按照GB/T 2951.11—2008测得的参考值进行线性拟合,结果如图10所示。结合表2图10可以看出,断裂伸长率和抗张强度的预测值均与参考值呈较好的线性关系。抗张强度的R2比断裂伸长率的R2略低,可能与样品抗张强度数据分布范围较窄、不同样品间抗张强度差异相对较小有关;当样品间真实差异接近单个样品测试离散程度时,模型线性相关性会受到一定影响。
选取5个样品作为盲样,按照建模阶段相同的光谱预处理方法对其近红外光谱数据进行处理,输入已建立的两个多元校正模型,预测其力学性能指标值,预测结果与参考值对比见表3表4。从表3表4可以看出,与3.1.2中参考方法重复误差数据相比,对于近红外光谱检测值,无论是绝对偏差还是相对偏差,均与参考方法的重复误差相当,表明本文建立的分析方法可以准确检测XLPE绝缘层的力学性能指标。
在60个样品中,选取断裂伸长率和抗张强度差异明显的3个代表性样品(30#、34#和50#)开展重复性试验,在每个样品上等距取3个采样点,每个采样点点采集3张光谱并取平均光谱,将其代入校正模型,得到3个力学性能预测值,如表5所示。
表5可以看出,对于这两个力学性能指标,近红外光谱方法检测精密度均与参考方法相当。
(1)基于透反射采集的XLPE绝缘层光谱标准差小于0.001 3,信噪比高,满足校正模型建模需求。
(2)XLPE位于1 360~1 390 nm和1 410~1 450 nm的谱带主要为-CH2-伸缩振动倍频与其弯曲振动倍频的组合频;位于1 660~1 800 nm谱带可能为-CH2-和-CH3伸缩振动的合频峰;不同XLPE样品近红外光谱的峰位置相同,峰强不同,可用于表征XLPE绝缘层组成变化。
(3)使用近红外光谱和PLS建立的模型预测的XLPE绝缘层力学性能指标结果与GB/T 2951.11—2008测试结果具有较好的一致性。
(4)基于近红外光谱技术,实现了电缆中XLPE绝缘层的断裂伸长率和抗张强度的同时检测和快速检测,其预测精度满足快速检测技术指标要求,可作为电力电缆现场到货或实验室快速筛查的技术手段。

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2026年第59卷第7期
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doi: 10.16790/j.cnki.1009-9239.im.2026.07.017
  • 接收时间:2025-07-08
  • 首发时间:2026-09-10
  • 出版时间:2026-07-20
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  • 收稿日期:2025-07-08
  • 修回日期:2025-09-17
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    1中国电力科学研究院有限公司,湖北 武汉 430074
    2电网环境保护全国重点实验室,湖北 武汉 430074
    3中电保力(北京)科技有限公司,北京 100020

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欧阳本红(1980-),男(汉族),湖北孝感人,教授级高级工程师,主要从事电力电缆结构设计、性能提升及运行维护技术的研究。
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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