A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization
This work provides a more efficient way to update ML models by removing poisoned data for engineers working on electrical equipment fault diagnosis, which is an incremental improvement over full retraining.
This paper addresses the problem of removing the influence of poisoned training data in machine learning models for power transformer inter-turn short-circuit fault localization without full retraining. They propose a SISA-based machine unlearning framework that achieves diagnostic accuracy almost identical to full retraining while significantly reducing retraining time.
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.