针对变压器匝间短路故障，提出高效数据清理框架，修复污染数据无需重训全模型。
A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization
- 将数据分片隔离训练，仅重训受污染片段
- 诊断准确率接近全量重训，提速显著
- 适合电力设备故障诊断中需快速响应的场景
在电气设备故障诊断的数据驱动应用中，传感器故障可能导致训练数据被污染，严重降低机器学习模型性能。但模型训练完成后，清除有害数据的影响极具挑战性，因全量重训既耗时又高成本。为此，本文提出一种基于SISA（分片、隔离、分切、聚合）的机器遗忘框架，用于电力变压器匝间短路故障（ITSCF）定位。该方法将训练数据分片并分切，通过独立训练使每个数据点的影响被隔离于特定子模型中。一旦检测到污染数据，仅需重训受影响的分片，避免从头训练整个模型。在模拟匝间短路条件下进行实验，结果表明该框架实现的诊断准确率几乎与全量重训相当，同时显著降低重训时间。
原文摘要 · Abstract (English)
In practical data-driven applications on electrical equipment fault diagnosis, training data can be poisoned by sensor failures, which can severely degrade the performance of machine learning (ML) models. However, once the ML model has been trained, removing the influence of such harmful data is challenging, as full retraining is both computationally intensive and time-consuming. To address this challenge, this paper proposes a SISA (Sharded, Isolated, Sliced, and Aggregated)-based machine unlearning (MU) framework for power transformer inter-turn short-circuit fault (ITSCF) localization. The SISA method partitions the training data into shards and slices, ensuring that the influence of each data point is isolated within specific constituent models through independent training. When poisoned data are detected, only the affected shards are retrained, avoiding retraining the entire model from scratch. Experiments on simulated ITSCF conditions demonstrate that the proposed framework achieves almost identical diagnostic accuracy to full retraining, while reducing retraining time significantly.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。