1 汇报安排

题 目：参加 IMECE 2015国际会议总结报告会

时 间：2015年11月26日（周四）下午19：00–21:00

地 点：西五楼 A328

报 告 人：博1116班 李兵 学号：4111001080 指导教师：张西宁 教授

2 参会论文信息

会议名称：ASME IMECE 2015

会议日期：13-19 November, 2015

会议地点：Houston, Texas, USA

会议简介：ASME’s International Mechanical Engineering Congress and Exposition (IMECE) is the largest interdisciplinary mechanical engineering meeting in the world. IMECE plays a significant role in stimulating innovation from basic discovery to translational application. It fosters new collaborations that engage stakeholders and partners not only from academia, but also from national laboratories, industry, research settings, and funding bodies. Among the 4,000 attendees from 75+ countries are mechanical engineers in advanced manufacturing, aerospace, advanced energy, fluids engineering, heat transfer, design engineering, materials and energy recovery, applied mechanics, power, rail transportation, nanotechnology, bioengineering, internal combustion engines, environmental engineering, and more.

3 参会论文信息

Tittle: Periodical Feature Extraction and Fault Diagnosis for Gearbox Using Local Cepstrum Technology

Author: Bing Li, Xining Zhang

Abstract- Results of numerous studies and experiments show that cepstrum analysis has the ability of simplifying the equally spaced sideband feature in the spectrum and highlights the signal components of defects. However, for most cases of early gear failure, the periodic phenomenon is always buried in strong background noises and the interference of the rotating frequency with its harmonics. Moreover, the features would be further weakened by the average effect of Fourier transform after cepstrum processing. In this paper, an improved cepstrum method named local cepstrum is proposed. The detection principle of local cepstrum is mainly based on the part of spectrum information to enhance the capability of extracting periodical features of detected signals. Besides, the autocorrelation and extended Shannon entropy function are also involved enhancing the periodic impulsive features. In the end, only several distinct lines with larger magnitudes would be left in the local cepstrum, which is very effective for gear fault detection and identification. Both simulation and experimental analysis show that the proposed method, which is more sensitive to the gear failure compared with conventional cepstrum analysis, could partially eliminate the interference of background noise and extract the periodical features of premature failure signals effectively.

欢迎有兴趣的同学届时参加！