Fisher discrimination multiple kernel dictionary learning for robust identification of nonlinear features in machinery health monitoring

• Published in: Information sciences 2024-08, Vol.677, Article 120862
• Main Authors: Zhang, Xin, Yang, Tao, Long, Hao, Shi, Haiyang, Wang, Jiaxu, Yang, Laihao
• Format: Article
• Language: English
• Subjects: Dictionary learning; Fisher discrimination; Machinery health monitoring; Multiple kernel learning; Nonlinear feature identification
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120862

•A Fisher discrimination multiple kernel dictionary learning (FDMKDL) is introduced.•Fisher discrimination is imposed on both high-dimensional samples and coding coefficients.•The kernel version of the classical Fisher discrimination dictionary learning is derived.•A multiple kernel learning framework is designed to promote the discriminability of samples.•FDMKDL shows good robustness, stability and adaptability in machinery health monitoring. In sparse representation-based classification, Fisher discrimination dictionary learning (FDDL) has attracted widespread attention due to its advantages such as fewer parameters and good dictionary discriminability. However, due to its linear nature, it is difficult to handle nonlinear features. Kernel-based nonlinear transformation enables dictionary learning to capture nonlinear features embedded in samples, but traditional single-kernel methods have limited performance. In this paper, a Fisher discrimination multiple kernel dictionary learning (FDMKDL) method is proposed, in which Fisher discriminative criterion is imposed on both high-dimensional samples and coding coefficients. Specifically, we derive the kernel version of FDDL, i.e., Fisher discrimination kernel dictionary learning (FDKDL) to learn nonlinear features and promote dictionary discriminability. Meanwhile, to avoid the problem of poor adaptability and possible manual selection caused by single-kernel methods, we further derive a flexible multiple kernel learning (MKL) framework, which utilizes the complementary information of multiple kernel functions to adaptively obtain the optimal weights and synthesize a discriminative kernel space. Finally, FDMKDL combines FDKDL with this MKL framework to obtain a more discriminative dictionary. Experimental evaluations conducted on two datasets demonstrate the effectiveness and robustness of the proposed FDMKDL method in learning nonlinear features for machinery health monitoring when compared to several state-of-the-art methods.