TY - GEN
T1 - An artificial intelligence based model for implementation in the petroleum storage industry to optimize maintenance
AU - Mushiri, T.
AU - Hungwe, R.
AU - Mbohwa, C.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Sporadic equipment breakdowns and unplanned downtime due to the predominant use of Reactive Maintenance and Preventive Maintenance at Company X necessitate the enhancement of the maintenance management system. This paper presents an Artificial Intelligence based model for optimizing the conventional maintenance strategies currently employed. Critical equipment at the fuel depot was identified through the Nowlan and Heap risk analysis matrix procedure. The critical equipment identified was pumps, storage tanks, valves and the standby power supply system. Ishikawa diagrams and FMECA analysis were then used in optimizing the Preventive Maintenance strategy and developing the Intelligent Maintenance model for each critical equipment. The focus of the AI Maintenance model was on pumps, as pumps were identified to be the most critical equipment. An Expert System was developed, tested and run for the pumps. The pump diagnosis application developed was programmed using Jess, a rule based system that accepts input from the operators.
AB - Sporadic equipment breakdowns and unplanned downtime due to the predominant use of Reactive Maintenance and Preventive Maintenance at Company X necessitate the enhancement of the maintenance management system. This paper presents an Artificial Intelligence based model for optimizing the conventional maintenance strategies currently employed. Critical equipment at the fuel depot was identified through the Nowlan and Heap risk analysis matrix procedure. The critical equipment identified was pumps, storage tanks, valves and the standby power supply system. Ishikawa diagrams and FMECA analysis were then used in optimizing the Preventive Maintenance strategy and developing the Intelligent Maintenance model for each critical equipment. The focus of the AI Maintenance model was on pumps, as pumps were identified to be the most critical equipment. An Expert System was developed, tested and run for the pumps. The pump diagnosis application developed was programmed using Jess, a rule based system that accepts input from the operators.
KW - Artificial Intelligence
KW - Expert Systems
KW - FMECA analysis
KW - Petroleum Storage Industry
KW - Predictive Maintenance
KW - Pumps
UR - http://www.scopus.com/inward/record.url?scp=85045282358&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2017.8290140
DO - 10.1109/IEEM.2017.8290140
M3 - Conference contribution
AN - SCOPUS:85045282358
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 1485
EP - 1489
BT - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
Y2 - 10 December 2017 through 13 December 2017
ER -