基于SISA的电力变压器匝间短路故障定位机器去学习框架
A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization
AI总结：
本文提出基于SISA的机器去学习框架，用于电力变压器匝间短路故障定位，通过分片和隔离技术减少重新训练时间，同时保持诊断准确性。
摘要：
在电力设备故障诊断的实用数据驱动应用中，训练数据可能因传感器故障而被污染，这会严重降低机器学习（ML）模型的性能。然而，一旦ML模型被训练，去除此类有害数据的影响具有挑战性，因为全面重新训练既计算密集又耗时。为了解决这一挑战，本文提出了一种基于SISA（分片、隔离、切片和聚合）的机器去学习（MU）框架，用于电力变压器匝间短路故障（ITSCF）定位。SISA方法将训练数据分成分片并进行切片，通过独立训练确保每个数据点的影响在特定构成模型中被隔离。当检测到污染数据时，仅重新训练受影响的分片，避免从头开始重新训练整个模型。在模拟的ITSCF条件下进行的实验表明，所提出的框架在诊断准确性上几乎与全面重新训练相同，同时显著减少了重新训练时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
