@inproceedings{dda08636c2cc41b386174647db27b3a2,
title = "Harmonics-to-noise ratio guided deconvolution and its application for bearing fault detection",
abstract = "In this paper, a precise and intelligent deconvolution process is developed in order to further extend its applications in discovering bearing faults and characteristics. A new index, harmonics-to-noise ratio (HNR), is introduced to extract the period. At the same time by combining HNR with the characteristic of kurtosis which is sensitive to the impulsivity of the fault signal, a novel deconvolution norm named HNR-guided deconvolution (HNRGD) is established to improve the performances of detecting the periodic impulses without requiring any prior knowledge. The effectiveness of the proposed method is validated by both simulation and experiment. The results demonstrate the superiority of HNRGD in the extraction and diagnosis of REBs faults compared with the original MED and MCKD.",
keywords = "Bearings, Deconvolution, Denoising, Fault diagnosis, Harmonic-to-noise ratio",
author = "Yonghao Miao and Ming Zhao and Jing Lin and Kaixuan Liang and Gang Liu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 8th IEEE Prognostics and System Health Management Conference, PHM-Harbin 2017 ; Conference date: 09-07-2017 Through 12-07-2017",
year = "2017",
month = oct,
day = "20",
doi = "10.1109/PHM.2017.8079259",
language = "英语",
series = "2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Bin Zhang and Yu Peng and Haitao Liao and Datong Liu and Shaojun Wang and Qiang Miao",
booktitle = "2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings",
}