Data-Driven Predictive Maintenance for Gas Distribution Networks

Wolfgang Betz, Iason Papaioannou, Tobias Zeh, Dominik Hesping, Tobias Krauss, Daniel Straub

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A generic data-driven approach is presented that employs machine learning to predict the future reliability of components in utility networks. The proposed approach enables utilities to implement a predictive maintenance strategy that optimizes life-cycle costs without compromising safety or creating environmental issues. Any machine learning technique that qualifies as a probabilistic classifier can be employed within the proposed approach. To identify the data-driven model that performs best, a practical metric to assess the performance of the competing models is proposed. This metric is specifically designed to quantify the forecasting performance with respect to maintenance planning. Additionally, a data-driven sensitivity analysis approach is discussed that allows for an assessment of the influence of the different features on the model prediction. Through an application example, it is demonstrated how the proposed approach can be applied to predict future defect rates of pipe sections for maintenance planning in a large gas distribution network.

Original languageEnglish
Article number04022016
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume8
Issue number2
DOIs
StatePublished - 1 Jun 2022
Externally publishedYes

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