Nonparametric Active Learning on Bearing Fault Diagnosis

Bearing plays decisive roles in modern industrial and electrical foundations. For authentic situation, immensely streaming and distributed data are congregated by Prognostics and Health Management (PHM) systems. The massive rigid data conduces the following puzzle: comparable huge excesses for PHM system, which is bounded on the whole huge sets. For this task, we employ active learning framework. In this paper, we firstly propose a novel nonparametric active learning (NAL) method and prove that NAL acquisition function is a tightly upper-bound of naive form. We validate our method on TCN (Temporal Convolutional Network) and achieve the state of the art performance on CWRU benchmark, providing mighty data effectiveness enhancement on industrial field.