Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification

Abstract

Standard ML relies on ample data, but limited availability poses challenges. Transfer learning offers a solution by leveraging pre-existing knowledge. Yet many methods require access to the model’s internal aspects, limiting applicability to white box models. To address this, Tsai, Chen and Ho introduced Black Box Adversarial Reprogramming for transfer learning with black box models. While tested primarily in image classification, this paper explores its potential in time series classification, particularly predictive maintenance. We develop an adversarial reprogramming concept tailored to black box time series classifiers. Our study focuses on predicting the Remaining Useful Life of rolling bearings. We construct a comprehensive ML pipeline, encompassing feature engineering and model fine-tuning, and compare results with traditional transfer learning. We investigate the impact of hyperparameters and training parameters on model performance, demonstrating the successful application of Black Box Adversarial Reprogramming to time series data. The method achieved a weighted F1-score of 0.77, although it exhibited significant stochastic fluctuations, with scores ranging from 0.3 to 0.77 due to randomness in gradient estimation.