LAUSR

Most existing fault detection methods rely on batch learning, which assumes that all data are available and can be accessed over and over again. Such an approach is impossible in the face of nonstationary environments. This article proposes an online combination of an Ensemble framework, Fuzzy Rough Active Learning, and drift detection, namely EFRAL, in which the structure of the ensemble automatically grows according to the data distribution. Since obtaining a real industrial data label is costly and time-consuming in practice, this article incorporates an active learning scenario based on fuzzy rough sets to reduce operator labeling effort. This method gives weight to training samples (TS) based on the fuzzy rough sets theory and the samples are selected based on their weight. The proposed algorithm is applied to detect a broken rotor bar (BRB) fault of induction motors to show its feasibility in industrial environments. The attained results show the efficiency of the proposed method. Furthermore, the efficiency of EFRAL is evaluated through data stream problems.