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A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization

1
College of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chao Yang District, Beijing 100029, China
2
Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(5), 445; https://doi.org/10.3390/e21050445
Received: 5 March 2019 / Revised: 7 April 2019 / Accepted: 26 April 2019 / Published: 28 April 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Abstract

In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time–frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time–frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm. View Full-Text
Keywords: Sparse non-negative matrix factorization; underdetermined blind source separation; compound faults diagnosis; time–frequency distribution Sparse non-negative matrix factorization; underdetermined blind source separation; compound faults diagnosis; time–frequency distribution
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Wang, H.; Wang, M.; Li, J.; Song, L.; Hao, Y. A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization. Entropy 2019, 21, 445.

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