Huang Wentao, Sun Hongjian, Wang Weijie
School of Mechatronics Engineering, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, China.
Sensors (Basel). 2017 Jun 3;17(6):1279. doi: 10.3390/s17061279.
Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.
机械设备是工业的核心。因此，机械故障诊断受到了广泛关注。鉴于故障振动信号中隐藏着丰富的信息，振动信号的处理与分析技术已成为机械故障诊断领域的一个关键研究问题。基于稀疏分解理论，塞莱斯尼克提出了一种新颖的非线性信号处理方法：基于共振的稀疏信号分解（RSSD）。自提出以来，RSSD已得到广泛认可，并且已经开发了许多基于RSSD的方法来指导机械故障诊断。本文试图总结和回顾RSSD在机械故障诊断中的理论发展和应用进展，并为那些对RSSD和机械故障诊断感兴趣的人提供更全面的参考。在简要介绍RSSD的理论基础之后，基于不同的优化方向，将RSSD在机械故障诊断中的应用分为五个方面：原始RSSD、参数优化的RSSD、子带优化的RSSD、综合优化的RSSD以及RSSD与其他方法相结合。在此基础上，还指出了当前RSSD研究中的突出问题以及相应的指导性解决方案。我们希望这篇综述能为对RSSD和机械故障诊断感兴趣的研究人员和读者提供有见地的参考。
