TY  - CONF
KW  - Cost effectiveness; Crude oil price; Frequency domain analysis; Gas industry; Induction motors; Knowledge based systems; Maintenance
KW  -  Capital intensive investment; Condition-based maintenance; Data-driven algorithm; Effective maintenance managements; Knowledge-based approach; Oil and Gas Industry; Signature extraction; Time and frequency domains
KW  -  Petroleum industry
N1  - cited By 7; Conference of 3rd International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2020 ; Conference Date: 29 January 2020 Through 30 January 2020; Conference Code:159464
TI  - Condition Based Maintenance of Oil and Gas Equipment: A Review
SN  - 9781728149707
ID  - scholars13887
A1  - Abbasi, T.
A1  - Lim, K.H.
A1  - Ahmed Soomro, T.
A1  - Ismail, I.
A1  - Ali, A.
Y1  - 2020///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084642693&doi=10.1109%2fiCoMET48670.2020.9073819&partnerID=40&md5=e26df4cc28955534df86ddeac1b8ffa4
AV  - none
PB  - Institute of Electrical and Electronics Engineers Inc.
N2  - Oil and gas industry requires capital-intensive investment especially in rotating mechanical equipment acquisition and installation. Rotating mechanical equipment such as induction motor, compressors and pumps, are essential components in industrial processes. The recent crude oil price drop raises the concern of effective maintenance management across oil and gas industry. Condition-based maintenance (CBM) is the most cost-effective maintenance technique to prevent the downtime of equipment and increases the productivity in petroleum industry. In this paper, recent reviews on CBM techniques for rotating equipment are presented under three categories, i.e. (1) Signature extraction-based method predicts machinery parameter in time and frequency domain, (2) Modelbased approach analyses machinery behavior in mathematical model and (3) Knowledge-based approach uses data-driven algorithm to learn system signal in the past for future prediction. The advantages, limitations and practical implication of each category are highlighted for suggestions and selection in the oil and gas industry. © 2020 IEEE.
ER  -