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Development of data-driven based pipeline leak detection system (LDS) Deep learning technology

Year: 2024 Authors: Leksono, E., Darmadi, Ihsan, M., A. Reference: IPA24-SS-08
Keywords: Artificial Intelligence (AI), deep learning, leak detection system, oil and gas, pipeline leak detection,
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Proceedings Title : Proc. Indon. Petrol. Assoc., 48th Ann. Conv., 2024

The need for pipeline leak detection systems arises from the critical importance of maintaining the integrity and safety of pipeline networks across v...

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Revolutionizing Gas Plant Proactive Maintenance with Advanced Deep Learning Anomaly Detection

Year: 2025 Authors: Wardana, F., K., Saputra, A., B. Reference: IPA25-E-324
Keywords: Anomaly detection, Early warning, Equipment failures, Gas processing plant, LSTM Auto Encoder, Leading Equipment Anomaly Detection System (LEADS), Operational Efficiency, Proactive maintenance, Real-time Data, deep learning,
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Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

Pertamina EP Cepu (PEPC) Jambaran Tiung Biru (JTB) located in Bojonegoro is a vital national gas production in Indonesia with a capacity of 192 MMSCFD...

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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...

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