Transfer Learning Framework for Remaining Useful Life Estimation

Training a robust deep learning (DL) model for remaining useful life (RUL) estimation or fault detection typically requires a large, high-quality labeled dataset. However, such datasets are often unavailable in practice. Transfer learning is a solution for smaller labelled datasets. Yet, the effectiveness of transfer learning heavily depends on selecting an appropriate source DL model; an unsuitable choice can result in negative transfer, where model performance deteriorates significantly. To address this challenge, we introduce REAPER (Reusable Neural Network Pattern Repository), a framework designed to assist users in selecting the most suitable DL model for reuse in transfer learning scenarios. REAPER analyzes and compares the characteristics of available datasets and employs a learned ranking model to recommend the optimal source model. This paper presents the architecture, including its dataset characterization, ranking methodology, training procedure, and practical usage guidance.