Lv Yong, Zhang Yi, Yi Cancan
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Entropy (Basel). 2018 Dec 1;20(12):920. doi: 10.3390/e20120920.
The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed.
滚动轴承早期故障信号的特征微弱，这导致特征提取困难。为了从轴承振动信号中诊断和识别故障特征，本文提出了一种基于排列熵的自适应局部迭代滤波器分解方法。作为一种新的时频分析方法，自适应局部迭代滤波克服了传统方法在模式分解中的两个主要问题：模态混叠和分量数量不确定。然而，自适应局部迭代滤波仍存在一些问题，主要是阈值参数的选择和分量数量。本文提出了一种基于粒子群优化和排列熵的改进自适应局部迭代滤波算法。首先，应用粒子群优化来选择自适应局部迭代滤波中的阈值参数和分量数量。然后，使用排列熵来评估我们所需的模式分量。为了验证所提方法的有效性，对轴承故障的数值模拟和实验数据进行了分析。
