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Vibration signal analysis is one of the most effective approaches for detecting faults in bearings. A bearing compound-fault signal always consists of multiple signatures and stochastic noise. The separation of multi-fault signals from them is not only crucial but also very challenging. In order to solve this issue, a novel multiple faults detecting technique has been developed based on tensor factorization. The original multi-channel vibration signals are formulated as a 3-way tensor via the temporal signal, spectra, and channel information in a high-dimensional space. The PARAFAC decomposition is used to analyze the tensor model, and then, principal component analysis (PCA) is introduced to find the numbers of the rank-one tensor. Finally, the tensor model is solved by alternating least squares (ALSs) approach combined with PCA technique. The performance of the detection method has been proven by simulation analysis of the multi-channel compound faults signals. The experimental results obtained using the developed technique also demonstrated that compound-fault signatures can be effectively and clearly identified.

A Tensor-Based Approach for Identification of Multi-Channel Bearing Compound Faults