Reliability assessment for CNC equipment based on degradation data

  • Chaoqun Duan
  • Chao DengEmail author
  • Ning Li


The computer numerical control (CNC) equipment is playing essential role in production and manufacturing, thus, its reliability estimation is crucial for industrial systems. Existing research studies mainly focused on univariate degradation data or multiple correlated data, which might not be accurate in equipment reliability assessment. In real CNC equipment, the degradation data consists of correlated and non-correlated degradation data and the data sample might be scarce, which bring great challenges for maintenance managers to assess risk and prevent failure ahead of fault occurrence. This paper proposes a reliability assessment approach based on multiple non-correlated and correlated degradation data with focusing on small size of data sample. Using the multiple degradation data of CNC equipment, a covariance matrix is built to determine the correlation between the performance signals. If the performance signals are not correlated, the degradation trajectory of the performance signals is curve fitted by least squares support vector machines (LS-SVM). The reliability which indicates the degradation curve not reaching its specified threshold is calculated. If the performance signals are correlated, according to the degradation data, the degradation curve of the performance signal is fitted by multivariate regression using LS-SVM, and the joint probability density function is derived. According to both the reliabilities of non-correlated degradation data and correlated degradation data, the joint reliability of the system is calculated. Finally, using a specific type of CNC machine tool as an example, the performance reliability assessment method is verified.


CNC equipment Reliability assessment Degradation data Condition monitoring Least squares support vector machines (LS-SVM) 


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The authors are grateful to the technical editor and all reviewers for their valuable and constructive comments.

Funding information

The research was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51605095; the National Key Research and Development Program of China under Grant No. 2016YFE0121700; and the Science and Technology Development Fund of Macao S.A.R. (FDCT) under MoST-FDCT Joint Grant No. 015/2015/AMJ.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.School of Mechatronics Engineering and Automation Shanghai UniversityShanghaiPeople’s Republic of China

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