较小样本动态声发射信号多元统计分析技术  

Analysis of Dynamic Acoustic Emission Signals Using Multivariate Statistical Technique for Smaller Dataset

作　　者：陈宏志[1,2] 芦永明[1] 王丽娜[1] Lik-Kwan Shark John Goodacre 

机构地区：[1]冶金自动化研究设计院混合流程工业自动化系统与装备技术国家重点实验室,北京100071 [2]Applied Digital Signal and Image Processing Research Centre,University of Central Lancashire Preston,United Kingdom,PR1,2HE [3]School of Health and Medicine,Lancaster University Lancaster,United Kingdom,LA1,4YD

出　　处：《振动．测试与诊断》2013年第2期199-203,335,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：英国Arthritis Research Campaign支持计划资助项目(17542);国家重点基础研究发展计划("九七三"计划)资助项目(2010CB736005)

摘　　要：利用多传感信息集成系统,以两组平均年龄对应的受试对象往复运动过程中获取的动态声发射信号和角度信号为对象,研究了适用于较小样本的动态声发射信号多元统计分析技术。通过同步记录的角度信号,将往复运动分解为若干个独立运动周期和运动过程;利用累计概率分布,选取具备较显著差异的特征;结合多元统计技术,减小数据量,建立动态声发射信号的可视化模型,证实了使用较小样本声发射信号实现膝盖骨关节诊断的可行性。A potential multivariate statistical based acoustic signal analysis and processing technique is presented for dynamic knee assessment for the smaller dataset.By using the integrated data acquisition system developed by the authors from two age-matched elder groups,the dynamic acoustic and the corresponding joint angle signals emitted from the consecutive knee movements are acquired.Consecutive movement cycles are isolated into individual for further analysis,and the cumulative probability distribution is employed for feature selection.Multivariate statistic methodologies are employed to derive the acoustic emission based joint profiles and to create the visual effect among the healthy and osteoarthritic groups,as well as to create the cluster evidence to demonstrate the feasibility of diagnosing knee osteoarthritis using the dynamic acoustics emission.The research findings show not only the potentials for simplifying the data acquisition protocol,but also the discovery of movement significancy.

关 键 词：较小样本 动态 声发射 多元统计 骨关节炎 

分 类 号：TN911.72[电子电信—通信与信息系统] R331[电子电信—信息与通信工程]