气味传感阵列在变质矿物油气味识别中的应用

Yuanchang Liu, Sosuke Akagawa, Rui Yatabe, Takeshi Onodera, Nobuyuki Fujiwara, Hidekazu Takeda, K. Toko
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摘要

变压器中矿物油的变质或氧化会造成短路的风险。预计将开发出方便有效的方法。本研究使用碳基传感器阵列来识别充油变压器矿物油的气味质量变化。气味传感系统的敏感层由不同类型的GC固定相材料和炭黑(CB)混合物组成。我们利用极性,通过气相色谱-质谱(GC/MS)分析,基于矿物油的不同成分,如烷烃和二甲苯,制作了一个传感器阵列,对GC材料进行了有针对性的选择。通过机器学习,利用敏感层的响应特性识别矿物油气味。以实验室空气为载气,该系统可以区分使用了20多年的矿物油和新矿物油,准确率约为93.8%。对于三种不同浓度的未使用矿物油和变压器泄漏产生的氧化矿物油,识别准确率约60%。当检测浓度超过50%的氧化矿物油时,准确率达到80%以上。本研究中的气味传感系统将有助于在短时间内检查变压器中的矿物油并做出泄漏判断。
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Odor recognition of deteriorated mineral oils using an odor-sensing array
The deterioration or oxidation of the mineral oil in transformers poses the risk of short circuits. Convenient and effective methods are expected to be developed. Carbon-based sensor arrays were used in this study to identify the quality variations of mineral oil for oil-filled transformers by odors. The sensitive layers of the odor-sensing system consisted of different types of GC stationary phase materials and carbon black (CB) mixtures. We made a targeted selection of GC materials by utilizing the polarities to make a sensor array based on the distinct components of mineral oil such as alkanes and xylenes by gas chromatography mass spectrometry (GC/MS) analysis. The response characteristics of the sensitive layers were used to recognize the mineral oil odors by machine learning. With laboratory air as the carrier gas, the system could distinguish mineral oil that has been in use for over 20 years from new mineral oil with an accuracy of about 93.8%. The identification accuracy achieved was about 60% for three different concentrations of unused mineral oil and the oxidized mineral oil created by the transformer’s leakage. When detecting the oxidized mineral oil with a concentration of more than 50%, the accuracy rate reached more than 80%. The odor-sensing system in this study will help inspect mineral oils in the transformer and make leakage judgments in a short time.
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