Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis
Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis
复制标题
压缩感知的压缩稀疏时频特征表示及其在故障诊断中的应用
DOI：
10.1016/j.measurement.2015.02.046
复制
发表时间：
2015-05-01
期刊：
影响因子：
5.6
通讯作者：
He, Shuilong
中科院分区：
文献类型：
--
作者：
Wang, Yanxue;Xiang, Jiawei;He, Shuilong
时频域特征提取在旋转机械故障诊断中有着广泛的应用。但是，它需要更多的时间和空间来存储时频信息，这限制了它的实际应用，特别是在远程健康监测方面。提出了一种新的基于压缩感知的并行FISTA类邻近分解算法，用于从有限的噪声观测值中重构稀疏时频表示（TFR）.通过数值仿真验证了该方法的有效性。所提出的方法比传统的RecPF方法得到更好的结果。然后，通过所提出的算法和无线通信的进步，开发了一种新的框架，用于远程机器健康状况监测。通过大量的实际案例，进一步验证了该方法在旋转机械轴承和齿轮缺陷检测中的有效性。这些结果表明，所提出的方法可以很好地保留TF签名没有明显的伪影，在恢复的TFR仅使用非常有限的测量。(C)2015爱思唯尔有限公司版权所有。
Feature extraction in time-frequency domain is wildly used in fault diagnosis of rotating machines. However, it needs more time and space to store the time-frequency information, which restricts its practical applications, especially for remote health monitoring. A novel parallel FISTA-like proximal decomposition algorithm was proposed for reconstruction of sparse time-frequency representation (TFR) from the limited noisy observations based on the recently developed compressive sensing. The effectiveness of recovering buried sparse signatures was demonstrated by numerical simulations. The proposed method yielded better results than those obtained by the traditional RecPF method. A novel framework for remote machine health condition monitoring was then developed via the proposed algorithm and the advancements in wireless communication. The effectiveness of the new proposed method for the sparse TFR in detecting bearings and gears defects in rotating machines is further verified using many practical cases. These results illustrate the proposed method can well retain TF signatures without clearly artifacts in the recovered TFR using only very limited measurements. (C) 2015 Elsevier Ltd. All rights reserved.