An analysis method for engine ignition waveform based on wavelet threshold denoising and secondary empirical mode decomposition

被引：0
|
作者：
Jiang, Shuxia [1 ,2 ]
Luo, Yiping [1 ]
机构：
[1] School of Traffic and Transportation Engineering, Central South University, Changsha,410075, China
[2] School of Traffic and Logistics, Central South University of Forestry and Technology, Changsha,410004, China
来源：
Qiche Gongcheng/Automotive Engineering | 2015年 / 37卷 / 04期
关键词：
Fault detection - Engines - Wavelet decomposition;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An engine waveform analysis method is proposed by combing wavelet threshold denoising with secondary empirical mode decomposition (SEMD). By analyzing the characteristics of eliminated noise, it is found that wavelet threshold denoising works better than SEMD denoising. SEMD-based time-frequency analysis can effectively analyze the abnormal components of ignition waveforms according to the features of intrinsic mode function (IMF). The method proposed takes full advantage of both wavelet threshold denoising and SEMD-based time-frequency analysis, and once the database of waveform is set up based on the method, the non-disassembling fault diagnosis of engine can be realized. ©, 2015, SAE-China. All right reserved.
引用
收藏
页码：380 / 386
相关论文
共 50 条
  • [1] A method for MT data denoising based on empirical mode decomposition and wavelet threshold
    Cai, Jianhua
    Wang, Xianchun
    Hu, Weiwen
    Cai, J. (cjh1021cjh@l63.com), 1600, Science Press (48): : 303 - 307
  • [2] Study of wavelet threshold denoising based on empirical mode decomposition
    Key Laboratory of Urban Security and Disaster Engineering, Beijing University of Technology, Beijing 100022, China
    不详
    Beijing Gongye Daxue Xuebao J. Beijing Univ. Technol., 2007, 3 (265-272):
  • [3] Empirical mode decomposition denoising method based on autocorrelation and threshold
    Wang, G. (jxxwgb@126.com), 1600, Advanced Institute of Convergence Information Technology, Myoungbo Bldg 3F,, Bumin-dong 1-ga, Seo-gu, Busan, 602-816, Korea, Republic of (07):
  • [4] Wavelet and Empirical Mode Decomposition Denoising for GLAS Full Waveform Data
    Wang Zhenhua
    Liu Xiaodan
    Liu Xiangfeng
    LASER & OPTOELECTRONICS PROGRESS, 2021, 58 (23)
  • [5] Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition
    Hu, Hongping
    Ao, Yan
    Yan, Huichao
    Bai, Yanping
    Shi, Na
    JOURNAL OF SENSORS, 2021, 2021
  • [6] A Denoising Module Based On the Wavelet and Empirical Mode Decomposition for Photoacoustic Microscopy
    Du, Yi
    Li, Lin
    Chai, Xinyu
    Zhou, Chuanqing
    OPTICS IN HEALTH CARE AND BIOMEDICAL OPTICS VI, 2014, 9268
  • [7] An effective electrocardiogram segments denoising method combined with ensemble empirical mode decomposition, empirical mode decomposition, and wavelet packet
    Yue, Yaru
    Chen, Chengdong
    Wu, Xiaoyuan
    Zhou, Xiaoguang
    IET SIGNAL PROCESSING, 2023, 17 (06)
  • [8] A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition
    Zhou Y.
    Hu Y.
    Li M.
    Yang L.
    Shang Z.
    Shang, Zhigang (zhigang_shang@zzu.edu.cn); Shang, Zhigang (zhigang_shang@zzu.edu.cn), 1600, West China Hospital, Sichuan Institute of Biomedical Engineering (37): : 271 - 279
  • [9] Sensitive method for detecting tooth faults in gearboxes based on wavelet denoising and empirical mode decomposition
    Jin, Shuai
    Kim, Jong-Sik
    Lee, Sang-Kwon
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2015, 29 (08) : 3165 - 3173
  • [10] The Denoising Method of OLTC Vibration Signals Based on Ensemble Empirical Mode Decomposition and Wavelet Thresholding
    Shi, Yanhui
    Yang, Yang
    Ruan, Yanjun
    Zhang, Tao
    Lin, Mingliang
    Luo, Zhao
    2024 IEEE 2ND INTERNATIONAL CONFERENCE ON POWER SCIENCE AND TECHNOLOGY, ICPST 2024, 2024, : 660 - 664
12345