Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming

• Published in: Energy (Oxford) 2018-09, Vol.158, p.485-494
• Main Authors: Safiyullah, F., Sulaiman, S.A., Naz, M.Y., Jasmani, M.S., Ghazali, S.M.A.
• Format: Article
• Language: English
• Subjects: Centrifugal compressors; Compressors; Computation; Computer applications; Computer programming; Diagnostic systems; Downtime; Empirical equations; Gas compressor; Gas compressors; Gases; Genetic algorithms; Genetic programming; Interpolation; Maintenance management; Maintenance prediction; Mathematical models; Offshore engineering; Oil and gas industry; Performance degradation; Performance prediction; Product reliability; Spare parts
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.06.051

In oil and gas industry, the performance prediction of gas compressors is approaching criticality. Usually, maintenance engineers rely on recommendations set by the original equipment manufacturer (OEM) for maintenance activities. Since compressors are operated in offshore conditions, OEM recommendations may over predict or under predict the maintenance schedule. An improper verdict on compressor maintenance interventions may increase the equipment downtime because of unavailability of the resources and poor readiness of the spare parts. The aim of the presented research was to develop a diagnostic model for gas compressors by using the genetic programming (GP). The OEM isentropic and actual isentropic heads were compared, and the maintenance activity of a gas compressor was predicted by calculating the performance degradation. The computational codes were developed separately for OEM isentropic and actual isentropic heads through GP. Hereinafter, the empirical equations were derived from the developed computational codes to predict the optimum time for the routine maintenance. For rotational speed between the tested regions, GP predicted 92% accurate interpolation between the curves. It reveals that using the developed GP model, the operators can accurately predict the compressor's health and plan ahead the equipment maintenance at any time. •Genetic programming was used to solve maintenance problems in rotary machines.•The health of the gas compressors in Malaysian oil and gas industry was forecasted.•Various degradation points were set to alert for maintenance interventions.•Degradation degree and optimum time were measured for decisions making.