Reliability Modeling of NC Machine tools Based on Artificial Intelligence

The level of reliability for NC machine tools represents the development level of the country's manufacturing industry, and its reliability modeling is very important. In order to improve the reliability of NC machine tools, this paper proposes the application of artificial intelligence model for NC machine toolreliability. Firstly, the time history of the NC machine tool reliability is analyzed. Secondly, a reliability evaluation framework based on neural network and bayesian network, intelligent fault diagnosis based on deep learning, and intelligent fault prediction framework based on least squares support vector machine (LS-SVM) are established to achieve remote maintenance of NC machine tools. Finally, with the background of big data, the development vision about the application of artificial intelligence model for NC machine toolreliability is summarized and forecasted.

[1]  Jason R. W. Merrick,et al.  A Bayesian Semiparametric Analysis of the Reliability and Maintenance of Machine Tools , 2003, Technometrics.

[2]  Weiwen Peng,et al.  A Bayesian optimal design for degradation tests based on the inverse Gaussian process , 2014 .

[3]  Gang Niu,et al.  Multi-agent decision fusion for motor fault diagnosis , 2007 .

[4]  Michael S. Hamada,et al.  A Demonstration of Modern Bayesian Methods for Assessing System Reliability with Multilevel Data and for Allocating Resources , 2009 .

[5]  Weiwen Peng,et al.  Life cycle reliability assessment of new products - A Bayesian model updating approach , 2013, Reliab. Eng. Syst. Saf..

[6]  Yaguo Lei,et al.  Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era , 2018 .

[7]  Ming Zhang Reciprocating Compressor Fault Diagnosis Technology Based on Multi-source Information Fusion , 2017 .

[8]  Zhigang Tian,et al.  An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring , 2012, J. Intell. Manuf..

[9]  Weiwen Peng,et al.  Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures , 2018, Reliab. Eng. Syst. Saf..

[10]  Theodoros H. Loutas,et al.  Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression , 2013, IEEE Transactions on Reliability.

[11]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[12]  Baoyu Wang,et al.  Numerical and experimental investigations on large-diameter gear rolling with local induction heating process , 2017 .

[13]  Yang Zhao-ju Progress in the Research of Reliability Technology of Machine Tools , 2013 .

[14]  Weiwen Peng,et al.  A Bayesian Approach for System Reliability Analysis With Multilevel Pass-Fail, Lifetime and Degradation Data Sets , 2013, IEEE Transactions on Reliability.

[15]  Gang Niu,et al.  Intelligent condition monitoring and prognostics system based on data-fusion strategy , 2010, Expert Syst. Appl..

[16]  Zhifeng Liu,et al.  An accuracy design approach for a multi-axis NC machine tool based on reliability theory , 2017 .