A Novel Feature Enhancement Method Based on Improved Constraint Model of Online Dictionary Learning

• Main Authors: Huaqing Wang, Pengxin Wang, Liuyang Song, Bangyue Ren, Lingli Cui
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Online dictionary learning; sparse representation; elastic-net; <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>₂,₁ norm; feature enhancement
• Online Access: https://ieeexplore.ieee.org/document/8630917/

Online dictionary learning (ODL) is an emerging and efficient dictionary learning algorithm, which can extract fault features information of fault signals in most occasions. However, the typical ODL algorithm fails to consider the interference of noise and the structural features of the fault signals, which leads to the fault features of weak fault signals that are difficult to extract. For that, a novel feature enhancement method based on an improved constraint model of an ODL (ICM-ODL) algorithm has been proposed in this paper. For the stage of dictionary learning, the elastic-net constraint is used to promote the anti-noise performance of the dictionary atoms. For the stage of signals sparse coding, the l_2,1 norm constraint is added to learn the structural features of fault signals. In addition, a variational mode decomposition algorithm is used to reduce the impact of noise on the signal initially. Taking the weak fault signals of bearing as examples for analysis, the results show that the feature enhancement of the weak fault signals is fulfilled by using the ICM-ODL algorithm. Compared with the typical ODL method, the ICM-ODL algorithm can not only improves the anti-noise performance of the dictionary atoms, but also removes the noise compositions of the reconstructed signal significantly.