The School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333, Long Teng Road, Shanghai, 201620, China.
Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran.
ISA Trans. 2018 Jul;78:98-104. doi: 10.1016/j.isatra.2017.12.021. Epub 2017 Dec 30.
In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data. After the sparse signal is reconstructed, the calculated signal is restored with the minimum entropy de-convolution (MED) to get clear fault information. Finally we adopt the sample entropy. Morphological mean square amplitude and the root mean square (RMS) to find the early fault diagnosis of bearing respectively, at the same time, we plot the Boxplot comparison chart to find the best of the three indicators. The experimental results prove that the proposed method can effectively identify the early weak fault diagnosis.
在海洋系统中，发动机是船舶最重要的部分，发动机中的轴承故障概率最高，因此在轴承振动分析中，早期微弱故障的检测对于长期监测非常重要。在本文中，我们提出了一种解决轴承早期弱故障诊断的新方法。首先，我们应该改进交替方向乘子法（ADMM），改变传统 ADMM 的结构，然后将改进的 ADMM 应用于压缩感知（CS）理论，实现了大量数据的轴承信号稀疏优化。稀疏信号重构后，采用最小熵反卷积（MED）恢复计算信号，以获取清晰的故障信息。最后，采用样本熵、形态均值平方幅度和均方根（RMS）分别对轴承进行早期故障诊断，并绘制箱线图对比图，找到这三个指标中的最佳值。实验结果证明，所提出的方法可以有效地识别早期微弱故障诊断。
