Early fault detection for rolling bearings: A meta‐learning approach

Early fault detection (EFD) of rolling bearings aims at detecting the early symptoms of faults by monitoring small deviations of health states. Accurate EFD enables predictive maintenance and contributes to the stability of mechanical systems. In recent years, machine learning based methods have shown impressive performance on EFD. Most of the current machine learning‐based methods assume the availability for a large amount of data. However, in practice, the authors may only have a very limited amount of training data, which makes it hard to learn a reliable machine learning model. To address this concern, in this work, the authors propose to tackle EFD via meta learning. Specifically, the authors first formulate EFD as a few‐shot learning problem and then propose to tackle this problem with a metric‐based meta learning method. Furthermore, ensemble learning is further leveraged to improve the detection robustness. For the proposed method, the distribution difference from the working conditions and the bearings are considered. The experimental results on two bearing datasets show that the proposed method can achieve better EFD performance, that is, detecting incipient faults earlier while bringing in lower false alarms, compared with several frequently used EFD methods. Considering the distribution drift problem along with the limited available data in early fault detection, the EFD task can be formulated to a Few‐shot Anomaly Detection problem. To solve this problem, a metric‐based meta learning method named Dynamic Relation Network with Voting Mechanism (DRNVM) is proposed. Furthermore, a dynamic support set updating strategy and ensemble learning are leveraged to improve the detection robustness.