Abstract
Early fault detection (EFD) presents significant challenges due to the subtle nature of abnormal signals during this phase. Traditional EFD methods primarily focus on identifying the onset time of failures. In this paper, we propose a novel EFD approach that simultaneously achieves early fault detection and health stage classification. We begin by developing a threshold-free fault detector utilizing adversarial learning, enabling the network to perform supervised classification and unsupervised fault detection concurrently. To further enhance the stability of the adversarial network, we introduce a straightforward ensemble learning technique. A pivotal aspect of this study is the conceptualization of the EFD problem as fault detection with continuously emerging new classes (FDENC), wherein different stages of a fault are treated as new classes. To support this framework, we investigate a self-model update mechanism based on the designed network. Unlike traditional EFD paradigms, our proposed method not only identifies the onset of early failures but also facilitates health stage classification, thereby indicating the progression of the fault. We evaluate the effectiveness of our approach through two run-to-failure experiments designed to induce sub-surface cracks. The experimental results demonstrate the method's sensitivity to the emergence and progression of micro-cracks in bearings. Additionally, we employed two benchmark datasets to assess the method's performance, further highlighting its superiority in both fault detection and health stage classification.