Class-Imbalanced Machinery Fault Diagnosis Using Heterogeneous Data Fusion Support Tensor Machine

Abstract

The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition, which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis. On the other hand, the vectorization of multi-source sensor signals may not only generate high-dimensional vectors, leading to increasing computational complexity and overfitting problems, but also lose the structural information and the coupling information. This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine (STM) driven by heterogeneous data fusion. The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters. A third-order heterogeneous feature tensor is designed based on multi sensors, frequency band components and statistical parameters. STM based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis. A series of comparative experiments verify the advantages of the proposed method.

Conflict of Interest Statement

The authors declare no conflicts of interest.