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Applicable Predictive Maintenance Diagnosis Methods in Service-Life Prediction of District Heating Pipes

Pakdad Pourbozorgi Langroudi*, Ingo Weidlich

*Corresponding author for this work

Abstract

Maintaining the supply chain in every industry is an important concern for the operators. The negative impacts of inappropriate maintenance could be discussed from different perspectives as well as capital loss, reputation loss, hazard and risk for lives, etc. In recent years, District heating (DH) in the countries that employing this technology broadly, turned to a vital energy infrastructure for delivering heat from suppliers to the consumers. Therefore, the reliability of the system is of high importance for the public interest. The transition from reactive maintenance to proactive maintenance have improved a lot the reliability to the system. Currently, many industries are exploiting different forms of artificial intelligence (AI) to predict the failures and plan for interventions to increase the system efficiency. In this paper the different methods of predictive maintenance have been reviewed and the compatibility to apply on a DH network has been discussed.

Original languageEnglish
Pages (from-to)294-304
Number of pages11
JournalEnvironmental and Climate Technologies
Volume24
Issue number3
DOIs
Publication statusPublished - 1 Nov 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 07 - Affordable and Clean Energy
    SDG 07 Affordable and Clean Energy

Keywords

  • Artificial neural networks
  • asset management
  • condition-based maintenance
  • machine learning
  • proactive maintenance
  • system reliability

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