Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning

TL;DR

This paper presents a novel approach combining Multi-State Degradation Models and Deep Reinforcement Learning to optimize sewer pipe maintenance, resulting in cost-effective strategies that adapt to pipe age and degradation levels.

Contribution

It introduces a new framework integrating MSDM and DRL for sewer pipe maintenance, demonstrating improved decision-making over traditional heuristics.

Findings

DRL-based strategies outperform heuristics in cost savings.

The model adapts maintenance actions based on pipe age and degradation.

Effective management reduces failure risk and maintenance costs.

Abstract

Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various complexities. Our study addresses two challenges within the Prognostics and Health Management (PHM) framework applied to sewer assets: modeling pipe degradation across severity levels and developing effective maintenance policies. We employ Multi-State Degradation Models (MSDM) to represent the stochastic degradation process in sewer pipes and use Deep Reinforcement Learning (DRL) to devise maintenance strategies. A case study of a Dutch sewer network exemplifies our methodology. Our findings demonstrate the model's effectiveness in generating intelligent, cost-saving maintenance strategies that surpass heuristics. It adapts its management strategy based on the pipe's age, opting for a passive approach for newer pipes and transitioning to active strategies for older ones to prevent failures and reduce costs. This research highlights DRL's potential in optimizing maintenance policies. Future research will aim improve the model by incorporating partial observability, exploring various reinforcement learning algorithms, and extending this methodology to comprehensive infrastructure management.