Analysis of horizontal machining center field failure data based on generalized linear mixed model—a case study

Authors

  • Fanmao Liu,

    1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China
    2. Hunan Provincial Key Laboratory of Mechanical Equipment Health Maintenance, Hunan University of Science and Technology, Xiangtan 411201, Hunan, People's Republic of China
    Search for more papers by this author
  • Haiping Zhu,

    Corresponding author
    1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China
    • School of Mechanical Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China
    Search for more papers by this author
  • Xinyu Shao,

    1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China
    Search for more papers by this author
  • Guibing Gao

    1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People's Republic of China
    Search for more papers by this author

Abstract

In order to assess the operational reliability of the horizontal machining center (HMC), field failure data for 14 HMCs over one year are collected from an engine plant of a large automobile manufacturing company in China. In contrast with the usual approach, which just pools the data from all copies under the assumption that each copy is modeled by the same power law processes (PLP), a new model based on the generalized linear mixed model (GLMM) is proposed for analyzing the failure data from all copies of HMC. A basic idea of this method is to assume heterogeneity among all copies of HMC; it is also found that the underlying model for each individual copy is a PLP model with different shape parameters and scale parameters in the GLMM model. This method can make inferences about both the population and each individual copy. Meanwhile, the modified Anderson–Darling test is adapted to the goodness-of-fit test of the model. The results of the analysis suggest that this method is effective to analyze reliability of HMC. Copyright © 2010 John Wiley & Sons, Ltd.

Ancillary