MEMS gas sensor arrays and specially designed pattern recognition systems are the main research directions in the field of modern sensing technology in the engineering, especially in the smart sensing and monitoring of faults in large power equipment such as oil-immersed transformers.In this paper, the MEMS sensor array composed by eight SWCNTs-based (pure, -OH functionalized, -COOH functionalized, -NH₂ functionalized by ethylenediamine, -NH₂ functionalized by aniline, Ni-coated, Pd-doped, ZnO-doped) sensing units was palced in the fault characteristic gases (H₂, CO, and C₂H₂) of oil-immersed transformers, and their gas-sensing characteristics were tested in single and mixed gas atmosphere.Combined with the DBN-DNN pattern recognition method, the qualitative identification and quantitative analysis of the sensor array in a mixed gas atmosphere was realized, and the accuracy and reliability of the results are higher than the traditional BPNN model.

MEMS气体传感器阵列和专门设计的模式识别系统是工程中现代传感技术领域的主要研究方向，特别是在油浸式变压器等大型电力设备的智能传感和故障监测中。由8周构成的MEMS传感器阵列的SWCNT基（纯，-OH官能化，官能化-COOH，-NH ₂由乙二胺官能化，-NH ₂官能由苯胺，Ni涂布，钯掺杂的，ZnO的掺杂的）感测单元是注入故障特征气体（H ₂，CO和C ₂ H ₂油浸式变压器），并在单一和混合气体气氛中测试了它们的气敏特性。结合DBN-DNN模式识别方法，实现了在混合气体气氛中传感器阵列的定性鉴定和定量分析，结果的准确性和可靠性高于传统的BPNN模型。