Compressed feature reconstruction for localized fault diagnosis with generalized minimax-concave penalty

作者：Deng, Lifa; Lin, Huibin^*; Liu, Zhongze; Wang, Hongchang

来源：MEASUREMENT, 2022, 200: 111622.

DOI：10.1016/j.measurement.2022.111622

• Compressed sensing
• Feature reconstruction
• Generalized minimax-concave penalty
• Localized fault

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.