基于多通道振动信号和视觉特征融合激发干型变压器的故障诊断方法
Yang Liu1, Mingtao Yu1, Jingang Wang2
1Baihetan Hydropower Plant, China Yangtze Power Co., Ltd., Liangshan 615400, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了一种新的,轻量级的故障诊断方法,通过融合多通道振动和视觉数据来激发干型变压器. 该方法达到94%的准确性,提高了关键电气设备的可靠性.
科学领域:
- 电气工程 电气工程
- 状态监控 状态监控
- 机器学习 机器学习
背景情况:
- 对于激发干型变压器的现有故障诊断方法在多轴振动数据利用,在复杂条件下的准确性和计算效率方面扎.
- 需要强大高效的方法来确保这些变压器的运行安全和可靠性.
研究的目的:
- 为激发干型变压器开发一种轻量级故障诊断方法.
- 提高多通道振动数据的利用率,提高识别精度和计算效率.
主要方法:
- 开发了一种用于激发干型变压器的多物理场合模拟模型.
- 扩展了对称点图案 (ISDP) 方法,从多轴振动数据创建二维特征图,使用粒子集群优化 (PSO) 进行优化.
- 使用面向快速和旋转简报 (ORB) 进行特征提取,并使用适应式提升支持矢量机 (Adaboost-SVM) 进行故障分类.
主要成果:
- 拟议的方法实现了94.00%的故障诊断准确度,用于典型的故障,如绕线松,核心松和离心率.
- 在准确性,培训和测试时间方面,超越了现有的信号到图像技术 (GAF,RP,MTF) 和深度学习模型 (CNN).
- 在重复的试验中证明了卓越的稳定性和强度,适合资源有限的环境.
结论:
- 轻量级的融合方法有效地解决了现有激发干型变压器故障诊断方法的局限性.
- 该方法为在线监控和快速诊断提供了强大,高效和准确的解决方案,提高了变压器的安全性和可靠性.
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