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A Bayesian Bivariate Degradation Analysis Method for Reliability Analysis of Heavy Duty Machine Tools

[+] Author Affiliations
Weiwen Peng, Yan-Feng Li, Jinhua Mi, Le Yu, Hong-Zhong Huang

University of Electronic Science and Technology of China, Chengdu, China

Paper No. IMECE2015-52603, pp. V014T08A010; 6 pages
doi:10.1115/IMECE2015-52603
From:
  • ASME 2015 International Mechanical Engineering Congress and Exposition
  • Volume 14: Emerging Technologies; Safety Engineering and Risk Analysis; Materials: Genetics to Structures
  • Houston, Texas, USA, November 13–19, 2015
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5757-1
  • Copyright © 2015 by ASME

abstract

The DL150 CNC heavy duty lathes can fulfill multiple heavy duties with high precision, which is one type of fundament manufacturing equipment. They are now serving as indispensable equipment in the industries of energy, transportation, aerospace and defense. To achieve high availability and productivity, unit-specific condition monitoring and degradation analysis are carried out. The machining accuracy and lubrication debris are observed as performance indicators. Due to these two indicators are depended on each other and the working profile of these heavy duty lathes varied greatly from factories to factories, a method for bivariate degradation analysis under dynamic conditions is urgent. However, among traditional degradation analysis method, two types of assumptions are generally adopted for degradation analysis: single degradation indicator and constant external factors. These methods can hardly characterize the degradation of complex systems that are subjected to multiple performance indicators under dynamic conditions. Originated from reliability analysis of DL 150 heavy duty lathes, this paper introduces a bivariate degradation analysis method. It is aimed to mitigate these two general assumption by addressing two practical engineering-driven issues, including: (1) a new types of bivariate models is introduced to deal with bivariate degradation processes modeling, and (2) two types of dynamic covariates are incorporated and treated separately within the proposed model to cope with dynamic condition modeling. Finally, a numerical example drawn from a type of heavy machine tools is presented to demonstrate the application and performance of the proposed method.

Copyright © 2015 by ASME

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