A Tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery

Abstract

Fault diagnosis of rolling bearings plays a pivotal role in modern industry. Most existing methods have two disadvantages: 1) The assumption that the training and test data obey the same distribution; and 2) They are designed for vector representation which is unable to characterize the important structure of the rolling bearings data of interest. To overcome these drawbacks, this paper proposes a novel tensor based domain adaptation method. Firstly, this method uses the time domain signals, the frequency domain signals, and the Hilbert marginal spectrum and integrates them into a third-order tensor model. Secondly, these three types of signals are split into two parts: the source and target domain data; all the representative features are identified in the source domain. Thirdly, a tensor decomposition method is used to decompose the features into a series of third-order tensors, and several alignment matrices are defined to align the representation of the two domains to the tensor invariant subspace. Then, the alignment matrices and the tensor subspace are jointly optimized to realize the adaptive learning. Finally, the feature tensor is reconstructed into a matrix form to realize the fault diagnosis through the classifier. Extensive experiments are conducted on a public dataset and a dataset collected from our own laboratory; experimental results show the satisfactory performance of the proposed method.