Hierarchical multiscale permutation entropy-based feature extraction and fuzzy support tensor machine with pinball loss for bearing fault identification

Abstract

Recently, tensor learning has been applied to intelligent bearing health monitoring successfully. In the past tensor learning based fault identification approaches, the clipped time–frequency images (TFIs) are usually taken as the input tensor data. However, the manual clipping method is usually used to reduce the dimension of TFIs, which may easily lose some critical fault information. Hence, we present hierarchical multiscale permutation entropy (HMPE) as a new feature representation method to avert this drawback from the perspective of statistical measure. In practice, the extracted fault feature (HMPE) may often be corrupted by noise. Hence, fuzzy support tensor machine with pinball loss (Pin-FSTM) as a new tensor-based classifier is developed, which can reduce noise sensitivity and minimize classification errors. Finally, a novel bearing health monitoring strategy based on HMPE and Pin-FSTM is presented, which has been demonstrated to have an excellent identification performance through two experiments. Compared with the state-of-the-art permutation entropy-based fault identification algorithms, the presented scheme is more superior in accuracy and stability. Besides, this research provides a new direction for tensor learning further applying in bearing health monitoring.