站单位故障诊断方法基于tssa-informer模型
Qingqing Tian1, Hongyu Yang1, Yu Tian2
1School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了改进的 Sparrow Search Algorithm (tSSA) 和Informer模型,用于站单元的智能故障诊断,在噪音和低数据条件下提高准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 站单元的故障诊断面临着噪声敏感性,长期时间序列依赖性和标记数据的高成本等挑战.
- 现有的方法在噪音条件下和有限的标记样本下难以保持准确性.
研究的目的:
- 开发一种智能故障诊断方法,克服噪声敏感性和数据限制.
- 改进对复杂工业设备的长期时间序列依赖性的建模.
主要方法:
- 开发了一种改进的 Sparrow 搜索算法 (tSSA) 与自适应的 t 分布策略,以提高优化准确性.
- 优化了Informer模型的长序列特征提取能力.
- 信息器模型的超参数通过使用 tSSA 进行了自适应优化.
- 数据预处理涉及异常值删除 (IQR) 和维度减少 (KPCA).
主要成果:
- 在无噪声条件下,SSA-Informer模型实现了98.73%的准确性,超过了SSA-Informer和GA-Informer.
- 在显著噪音 (SNR = -1 dB) 的情况下,该模型保持了87.47%的精度,超过了1D-DCTN.
- 只有10%的标记数据,精度达到61.32%,比传统模型高出40%.
结论:
- 拟议的 tSSA-Informer 方法在强噪声和小样本场景中表现出稳定性和实用性.
- 这种方法为复杂的工业设备的智能故障诊断提供了有效的解决方案.
- 该研究强调了将先进的优化算法与工业诊断的深度学习模型集成的潜力.
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