Abstract
Reciprocating compressors are widely used in oil and gas industry for gas transport, lift and injection. Critical reciprocating compressors that operate under high-speed conditions, flow hazardous gases and have a high duty cycle are target rotating equipment on maintenance improvement lists due to downtime risks and safety hazards. Estimating performance deterioration and failure time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. This study presents an application of Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models to estimate failure degradation and remaining useful life based on sensory data acquired from an operational industrial reciprocating compressor. CVA was used to extract a one-dimensional health indicator from the multivariate data sets, thereby reducing the dimensionality of the original data matrix. The failure rate was obtained by using the CPHM based on historical failure times. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and the one-dimensional performance measures obtained from the CVA model. The trained SVR model was then utilized to estimate the failure degradation rate and remaining useful life. The results indicate that the proposed method can be effectively used in real industrial processes to predict performance degradation and failure time.
Highlights
Modern industrial facilities such as natural-gas processing plants are becoming increasingly complex and large-scale due to the use of machines of different nature
Both the failure rate vectors calculated by the Cox Proportional Hazard Model (CPHM) and the one-dimensional health indicators obtained from the canonical variable analysis (CVA) model are regarded as target vectors indicating the health condition of the compressor under study
CVA combined with CPHM and Support Vector Regression (SVR) were applied for the first time on data collected from an operational industrial reciprocating compressor to perform prognostics
Summary
Modern industrial facilities such as natural-gas processing plants are becoming increasingly complex and large-scale due to the use of machines of different nature. The effectiveness of CVA in real complex industrial processes has not been fully studied In this investigation, CVA is adopted to transform the high-dimensional data from the sensors distributed over the machine to a one-dimensional matrix called the health indicator that can be used to indicate the health condition of a system. Censored run-to-failure data is used to estimate the baseline survival function and the temperature measurements obtained at the fault location is assumed as a time-dependent covariate to investigate the failure time distribution Both the failure rate vectors calculated by the CPHM and the one-dimensional health indicators obtained from the CVA model are regarded as target vectors indicating the health condition of the compressor under study. The SVR model is utilized to predict the failure degradation and failure time of individual failure sample given unseen values of input
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