通过偏差校准进行长尾类增量学习,适用于连续故障诊断
IEEE transactions on neural networks and learning systems
|September 8, 2025
概括
对于持续故障诊断 (CFD) 的阶级增量学习 (CIL) 与不平衡的工业数据作斗争. 一种新的方法,LTCIL-BC,校准偏差,以改善学习困难的故障类和减少遗忘.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 工业故障诊断 产业故障诊断
背景情况:
- 类增量学习 (CIL) 能够通过流数据进行连续故障诊断 (CFD).
- 现实世界的工业数据往往表现出长尾分布,挑战现有的CIL方法.
- 长尾偏差在增量模型中对知识保留和新任务学习产生负面影响.
研究的目的:
- 调查数据流偏差对CIL对CFD的影响.
- 提出一种新的CFD方法,长尾CIL通过偏差校准 (LTCIL-BC),以解决偏差.
- 提高偏见冲突样本的学习,并减轻灾难性遗忘.
主要方法:
- 对CIL的长尾偏差影响的实验分析.
- 开发LTCIL-BC,涉及同时培训非基于和偏向网络.
- 引入用于模型和数据偏差评估的偏差指示得分.
- 在偏差网络中的逻辑调整,以偏差指示得分为指导.
主要成果:
- 长尾偏差级联,影响旧知识的保留和新任务的学习.
- 增量模型与偏见冲突样本作斗争.
- 对于CFD,LTCIL-BC在长尾CIL中表现优越.
- 在PS和SWaT数据集上,比最先进的基线提高了多达9%.
结论:
- 通过校准,LTCIL-BC有效地解决了数据和模型偏差.
- 该方法优先考虑学习偏见冲突样本.
- 在不平衡的工业场景中,LTCIL-BC显著改善了连续故障诊断.
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