Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis
Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis
复制标题
基于局部均值分解和改进自适应多尺度形态学分析的液压泵故障信号解调
DOI：
10.1016/j.ymssp.2014.10.017
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发表时间：
2015-06-01
影响因子：
8.4
通讯作者：
Li, Yang
中科院分区：
文献类型：
--
作者：
Jiang, Wanlu;Zheng, Zhi;Li, Yang
该方法利用信号的形态特征自适应地确定IAMMA的尺度，从而实现了对液压泵故障信号多尺度故障特征的自适应解调。在一定的系数范围内，IAMMA在相同SE的基础上解调能力优于AMMA，且对噪声的敏感性低于AMMA。在对同一故障信号进行解调时，三角形SE的IAMMA比平面和半圆形SE的IAMMA具有更好的解调性能。与传统的HT和TKEO解调方法相比，IAMMA具有自适应性和更强的解调能力。提出了一种基于峰度、幂次和标准差的故障特征值评估方法，通过该方法可以选择故障特征丰富的特征值作为数据源。(C)2014爱思唯尔有限公司版权所有。
Scales of IAMMA are adaptively determined by morphological features of signal, thus fault features of a hydraulic pump fault signal presented in multi-scales can be adaptively demodulated. In some coefficient range, IAMMA outperforms AMMA in demodulation ability based on the same SE, and it is less susceptible to noises than AMMA. The best performance of IAMMA with triangle SE is stronger than that of IAMMA with plat and semi-circle SE when they demodulate the same fault signal of hydraulic pump. Compared with traditional demodulation methods of HT and TKEO, IAMMA is adaptive and has stronger demodulation ability. An evaluation method based on kurtosis, power and standard deviation is proposed, by which some PFs which are rich in fault features can be selected as data source. (C) 2014 Elsevier Ltd. All rights reserved.