Publications | Indonesian Petroleum Association

Extending Engine Change Out Respective to Running Hours Using Data Driven Using One Dimension (1-D) Convolutional Neural Network Algorithm

Year: 2024 Authors: Malik, S., Soedarsono, H., P. Reference: IPA24-SS-05

Keywords: GE Turbine LM2500+, Gas Generator Exhaust Temperature, Machine Learning, engine change out, preventive maintenance,

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Proceedings Title : Proc. Indon. Petrol. Assoc., 48th Ann. Conv., 2024

The current practice for engine change out of GE Turbine LM2500+ SAC, set at 25,000 for Hot Section Inspection, as per Instruction Operation and Maint...

A Machine Learning Approach for Early Fault Detection and Failure Prediction in Gas Turbines with Case Study: Avon 200 Combustor Crack and SOLAR Taurus 60-T7802 Thrust Balance Damage

Year: 2025 Authors: Fernanda, E., Arief, E., P., Afianto, D., Dadan, S. Reference: IPA25-F-395

Keywords: Anomaly detection, Condition-Based Monitoring, Failure Prediction, Gas Turbines, LSTM Auto Encoder, Machine Learning, Predictive modeling, Reliability, deep learning, preventive maintenance,

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Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

The critical in our oil and gas company as a failure can led to significant production and financial losses are gas turbines. Based on our operational...

Turbo Machinery Campaign Team As An Improvement of Preventive Maintenance for Turbomachinery Equipment at MEPG

Year: 2025 Authors: Suliharti, Firdaus, Tiansa, Y. Reference: IPA25-F-540

Keywords: Asset reliability, Campaign maintenance, Downtime reduction, Loss production opportunity, Maintenance strategy, Multidisciplinary team, Operational Efficiency, Plant performance, Turbomachinery, preventive maintenance,

Basins:

Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

Traditional PM strategies often suffer from scattered resources, uncoordinated scheduling, low wrench time, different standard execution and extended...

Result for 3 item(s)

Extending Engine Change Out Respective to Running Hours Using Data Driven Using One Dimension (1-D) Convolutional Neural Network Algorithm

A Machine Learning Approach for Early Fault Detection and Failure Prediction in Gas Turbines with Case Study: Avon 200 Combustor Crack and SOLAR Taurus 60-T7802 Thrust Balance Damage

Turbo Machinery Campaign Team As An Improvement of Preventive Maintenance for Turbomachinery Equipment at MEPG