Weak Supervision: A Survey on Predictive Maintenance

Abstract

The maintenance advancements achieved in Industry 4.0 generate large amounts of data, necessitating complete, accurate, and precise labels for training datasets to align with corresponding ground truth. These labels serve as annotations for early anomaly detection. Delivering high-quality annotations derived from weak labels and striking a balance between annotation efforts and accuracy are critical tasks. Consequently, researchers have focused their attention on Weakly Supervised Learning methods, which have shown effectiveness in handling datasets characterized by incomplete, imprecise, and erroneous labels across various maintenance applications. In this survey, the authors aim to address a gap in the existing literature by conducting a comprehensive examination of Weakly Supervised Learning for Predictive Maintenance, categorizing related works. Furthermore, the survey discusses challenges and identifies open research lines.