Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion.

Area of Science:

• Engineering
• Artificial Intelligence
• Materials Science

Background:

• Gas pipeline systems are critical global energy infrastructure, facing risks like corrosion.
• Corrosion in pipelines leads to significant risks, including ruptures and environmental leakage.
• Corrosion-related maintenance constitutes a substantial operational cost for pipeline systems.

Purpose of the Study:

• To investigate the application of reinforcement learning (RL) for optimizing corrosion-related maintenance in dry gas pipelines.
• To develop a simulated testbed for modeling corrosion degradation and RL interaction.
• To propose a data-driven, condition-based maintenance management approach using RL.

Main Methods:

• Development of a simulated test bench modeling pipeline corrosion within an RL environment.
• Implementation of a condition-based maintenance management strategy driven by RL.
• Application of an RL maintenance scheduler to the proposed test bench.

Main Results:

• The proposed RL-based condition-based maintenance management reduced maintenance costs by up to 58% compared to periodic maintenance.
• The RL approach effectively managed corrosion degradation and ensured pipeline reliability.
• The developed test bench successfully simulated corrosion and facilitated RL-based decision-making.

Conclusions:

• Reinforcement learning offers a powerful, data-driven methodology for optimizing pipeline maintenance strategies.
• Condition-based maintenance informed by RL can lead to substantial cost savings and enhanced operational safety.
• The proposed RL framework provides a viable solution for proactive corrosion management in gas pipelines.