Blind Extraction of Correlated Fault Sources Based on Constrained Non-Negative Matrix Factorization

Abstract

Blind source separation (BSS) has been successfully used to extract undetected fault vibration sources from mixed observation signals by assuming that each unknown vibration source is mutually independent. However, conventional BSS algorithms cannot address the situation in which the fault source could be partially dependent on or correlated to other sources. For this, a new matrix decomposition method, called Non-negative Matrix Factorization (NMF), is introduced to separate these partially correlated signals. In this paper, the observed temporal signals are transformed into the frequency domain to satisfy the non-negative limit of NMF. The constraint of the least correlation between the separated sources is added into the cost function of NMF to enhance the stability of NMF, and the constrained non-negative matrix factorization (CNMF) is proposed. The simulation results show that the separation performance of CNMF is superior to the common BSS algorithms and the experiment result verifies the practical performance of CNMF.