Reciprocating compressor prognostics

Abstract

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.