Reliability Assessment of CNC Machining Center Based on Weibull Neural Network

CNC machining centers, as the key device in modern manufacturing industry, are complicated electrohydraulic products. The reliability is the most important index of CNC machining centers. However, simple life distributions hardly reflect the true law of complex system reliability with many kinds of failure mechanisms. Due to Weibull model’s versatility and relative simplicity and artificial neural networks’ (ANNs) high capability of approximating, they are widely used in reliability engineering and elsewhere. Considering the advantages of these two models, this paper defined a novel model: Weibull neural network (WNN). WNN inherits the hierarchical structure from ANNs which include three layers, namely, input layer, hidden layer, and output layer. Based on more than 3000 h field test data of CNC machining centers, WNN has been successfully applied in comprehensive operation data analysis. The results show that WNN has good approximation ability and generalization performance in reliability assessment of CNC machining centers.

[1]  Tongmin Jiang,et al.  A prediction method of life and reliability for CSALT using Grey RBF neural networks , 2009, 2009 16th International Conference on Industrial Engineering and Engineering Management.

[2]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[3]  John H. K. Kao A Graphical Estimation of Mixed Weibull Parameters in Life-Testing of Electron Tubes , 1959 .

[4]  Hai Jun Wang,et al.  The Application of Artificial BP Neural Networks and Monte-Carlo Method for the Reliability Analysis on Frame Structure , 2012 .

[5]  W. Weibull A Statistical Distribution Function of Wide Applicability , 1951 .

[6]  Mitchell J. Mondro Approximation of mean time between failure when a system has periodic maintenance , 2002, IEEE Trans. Reliab..

[7]  Swagata Nandi,et al.  An EM algorithm for estimating the parameters of bivariate Weibull distribution under random censoring , 2010, Comput. Stat. Data Anal..

[8]  Zhang Genbao Likelihood Ratio Test Interval Estimation of Reliability Indices for Numerical Control Machine Tools , 2012 .

[9]  万 位水,et al.  Improving generalization ability of neural networks , 2002 .

[10]  Kay Chen Tan,et al.  Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks , 2008, IEEE Transactions on Neural Networks.

[11]  Nam Mai-Duy,et al.  A collocation method based on one-dimensional RBF interpolation scheme for solving PDEs , 2007 .

[12]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[13]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[14]  John E. Moody,et al.  Fast Learning in Multi-Resolution Hierarchies , 1988, NIPS.

[15]  J. P. Spivey,et al.  Probabilistic Reserves Estimation Using Decline Curve Analysis with the Bootstrap Method , 1996 .

[16]  Eric R. Ziegel,et al.  System Reliability Theory: Models, Statistical Methods, and Applications , 2004, Technometrics.

[17]  Thierry Denœux Maximum likelihood estimation from fuzzy data using the EM algorithm , 2011 .

[18]  Simon Haykin,et al.  Neural networks , 1994 .

[19]  F. Downton Bivariate Exponential Distributions in Reliability Theory , 1970 .

[20]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[21]  J. I. McCool Competing Risk and Multiple Comparison Analysis for Bearing Fatigue Tests , 1978 .

[22]  Peng Wang,et al.  Teaching distribution system reliability evaluation using Monte Carlo simulation , 1999 .

[23]  Abdallah W. Aboutahoun,et al.  A new approach for parameter estimation of finite Weibull mixture distributions for reliability modeling , 2013 .

[24]  Matija Fajdiga,et al.  Reliability approximation using finite Weibull mixture distributions , 2004, Reliab. Eng. Syst. Saf..

[25]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[26]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[27]  M. Moeschberger,et al.  Survival Models and Data Analysis , 1980 .

[28]  ESTIMATION OF PARAMETERS OF MIXED EXPONENTIALLY DISTRIBUTED FAILURE TIME DISTRIBUTIONS FROM CENSORED LIFE TEST DATA , 1958 .