CoWS: Self-Supervised Representation Pre-Training for Cross-Machine Fault Diagnosis

Abstract

Deep learning-based techniques have emerged as powerful tools for fault diagnosis. However, conventional deep learning methods require designing and training fault diagnosis models from scratch for each machine, necessitating a large-scale and high-quality labeled dataset. This limitation hinders the application of deep learning in practical settings, where labeled data is scarce and data distribution varies across different machines. To address these challenges, a novel self-supervised learning framework specifically designed for time-series data is proposed, aimed at enhancing cross-machine downstream fault diagnosis tasks with limited data. The proposed framework exploits inherent consistency between waveform and spectrogram representations, learning robust and transferable features from unlabeled data. Experimental results across three cross-machine fault diagnosis scenarios demonstrate that the proposed method outperforms existing state-of-the-art self-supervised methods, significantly reducing the reliance on labeled data and improving diagnostic performance.