Reciprocating compressor prognostics

Reciprocating compressors are vital components in oil and gas industry though their maintenance cost can be high. The valves are considered the most frequent failing part accounting for almost half the maintenance cost. Condition Based Maintenance and Prognostics and Health Management which is based on diagnostics and prognostics principles can assist towards reducing cost and downtime while increasing safety and availability by offering a proactive means for scheduling maintenance. Although diagnostics is an established field for reciprocating compressors, there is limited information regarding prognostics, particularly given the nature of failures can be instantaneous. This paper compares several prognostics methods (multiple liner regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)) using valve failure data from an operating industrial compressor. Moreover, a variation about Remaining Useful Life (RUL) estimation process based on KNNR is proposed along with an ensemble method combining the output of all aforementioned algorithms. In conclusion it is showed that even when RUL is relatively short given the instantaneous failure mode, good estimates are feasible using the proposed methods.

[1]  Uwe Kruger,et al.  Statistical monitoring of complex multivariate processes : with applications in industrial process control , 2012 .

[2]  Arto Haara,et al.  Comparing K Nearest Neighbours Methods and Linear Regression - Is There Reason To Select One Over the Other? , 2012, Math. Comput. For. Nat. Resour. Sci..

[3]  Mohamed Benbouzid,et al.  A regression algorithm for the smart prognosis of a reversed polarity fault in a photovoltaic generator , 2014, 2014 First International Conference on Green Energy ICGE 2014.

[4]  William A. Griffith,et al.  Online, Continuous Monitoring Of Mechanical Condition And Performance For Critical Reciprocating Compressors. , 2001 .

[5]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[6]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..

[7]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[8]  Renzo Capitani,et al.  Numerical and Experimental Testing of Composite Rings for Reciprocating Compressor Valves , 2013 .

[9]  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.

[10]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[11]  G. Meng,et al.  A generalized similarity measure for similarity-based residual life prediction , 2011 .

[12]  Piermaria Corona,et al.  Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems , 2008 .

[13]  Yi-Guang Li,et al.  Gas turbine performance prognostic for condition-based maintenance , 2009 .

[14]  Sankalita Saha,et al.  Evaluating algorithm performance metrics tailored for prognostics , 2009, 2009 IEEE Aerospace conference.

[15]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[16]  B. Tang,et al.  Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction , 2016 .

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

[18]  Jianbo Yu,et al.  A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.

[19]  Gang Xiong,et al.  A k-nearest neighbor locally weighted regression method for short-term traffic flow forecasting , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[20]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[21]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[22]  Puqiang Zhang,et al.  Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery , 2014 .

[23]  Zheng-Wu Yuan,et al.  Research on K Nearest Neighbor Non-parametric Regression Algorithm Based on KD-Tree and Clustering Analysis , 2012, 2012 Fourth International Conference on Computational and Information Sciences.

[24]  I. Jolliffe.  Principal Component Analysis , 2002 .