A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization

TL;DR

This paper introduces a SISA-based machine unlearning framework for power transformer fault localization that efficiently removes the influence of poisoned data without full retraining, maintaining accuracy and saving time.

Contribution

It proposes a novel SISA-based unlearning method that isolates data influence, enabling targeted retraining for fault diagnosis in electrical transformers.

Findings

Achieves diagnostic accuracy comparable to full retraining.

Reduces retraining time significantly.

Effectively isolates poisoned data influence.

Abstract

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.