• DocumentCode
    2470600
  • Title

    Outlier test and analysis method of degradation data under unequal error variances

  • Author

    Zhang, Huirui ; Chen, Yunxia ; Lin, Fengchun

  • Author_Institution
    Sch. of Reliability & Syst. Eng., Beihang Univ., Beijing, China
  • fYear
    2012
  • fDate
    23-25 May 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    For degradation data, it´s usually difficult to judge and elimination outlier data, especially when error variances are unequal. So, in the paper, under the assumption that the degradation data obey the same path model form, a degradation outlier test method is presented based on path model when the error variances are unequal. Since the path model gives the trend term of degradation data, its parameters can be considered as the degradation characterization. If the path model parameters are equal for two groups of degradation data, it shows that the two group data have the same degradation trend term. Otherwise, they have different trend terms, and the data of small size can be considered as outlier. For abnormal degradation data, an outlier analysis method is also proposed. It can test the differences in intercepts and slopes between two degradation path models, and determine whether the abnormity is caused by the differences in intercepts, slopes, or both. At the end, an example is given.
  • Keywords
    reliability theory; statistical testing; degradation data; degradation outlier test method; degradation path model; outlier analysis; outlier analysis method; outlier data; outlier test; unequal error variance; Degradation; Wald test; degradation; heteroscedasticity; linear regression; outlier test;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Prognostics and System Health Management (PHM), 2012 IEEE Conference on
  • Conference_Location
    Beijing
  • ISSN
    2166-563X
  • Print_ISBN
    978-1-4577-1909-7
  • Electronic_ISBN
    2166-563X
  • Type

    conf

  • DOI
    10.1109/PHM.2012.6228909
  • Filename
    6228909