Compressed feature reconstruction for localized fault diagnosis with generalized minimax-concave penalty. (15th August 2022)

• Record Type: Journal Article
• Authors: Deng, Lifa; Lin, Huibin; Liu, Zhongze; Wang, Hongchang
• Is Part Of: Measurement. Volume 200(2022)
• Publication Date: 2022-08-15
• Subjects: Compressed sensing -- Feature reconstruction -- Generalized minimax-concave penalty -- Localized fault; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111622
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel compressed feature reconstruction method is proposed. GMC penalty is utilized for improving the reconstruction accuracy. A segmented compression and reconstruction strategy is adopted. Regularization parameter is determined based on sparse coefficient proxy. It can be used for detecting the localized fault. Abstract: Compressed sensing can effectively relieve the burden of data storage and transmission for mechanical condition monitoring. However, feature reconstruction from compressed observations has always been a challenge. A novel compressed feature reconstruction method for the impulse response signal caused by localized damage is proposed based on the generalized minimax-concave (GMC) penalty. To improve the efficiency of compression and reconstruction, the fault signal is divided into several segments before compression, and the segmented sparse coefficient is obtained by solving a GMC problem. The fault-caused impulse signal is obtained by combining all the reconstructed segments together. Furthermore, a strategy of regularization parameter selection based on the sparse coefficient proxy is presented. Compared with the commonly used l 1 regularization-based reconstruction method, the proposed method can better preserve the amplitudes of impulse features. The effectiveness of the proposed method is further verified by simulation and experimental data.