Incremental Data-Driven Fault Diagnosis for Conveyor Rollers With Prior Drift Adaptation: An Online Active Kernel Learning Approach

Abstract

Conveyor rollers are essential components in the hot steel milling process, responsible for transporting products between various stages of production throughout the entire process. Due to the development of Industrial Internet of Things (IIoT), conveyors are now equipped with sensors to collect real-time data, facilitating data-driven fault diagnosis models. While many well-trained data-driven diagnostic models initially demonstrate accurate performance, their effectiveness may decline over time due to unexpected changes, such as the emergence of new faults. This causes the prior distribution of fault data to change over time, and we refer to this as prior drift. In this study, we provide a mathematical definition of prior drift within the context of fault diagnosis and then develop a general learning criterion to guide the incremental updating of fault diagnosis models under such conditions. Building on this criterion, we propose an online active kernel learning-based fault diagnosis model (OAKL) for conveyor rollers. OAKL updates the diagnosis model in an incremental manner and adapts to prior drift. Particularly, it leverages selective querying and labeling of the most informative samples to facilitate early detection of novel faults, avoid performance bias, and reduce the number of incremental samples required. A rigorous theoretical analysis is conducted to demonstrate the theoretical validity of OAKL. The experimental results on a practical industrial case demonstrate the efficacy of our approach in fault diagnosis.