<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Environmental and Climate Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Maintaining the supply chain in every industry is an important concern for the operators. The negative impacts of inappropriate maintenance could be discussed...

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

Data publikacji: 14 gru 2020

Zakres stron: 294 - 304

DOI: https://doi.org/10.2478/rtuect-2020-0104

Słowa kluczowe
Artificial neural networks, asset management, condition-based maintenance, machine learning, proactive maintenance, system reliability

© 2020 Pakdad Pourbozorgi Langroudi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Data publikacji: 14 gru 2020

Zakres stron: 294 - 304

DOI: https://doi.org/10.2478/rtuect-2020-0104

Słowa kluczoweArtificial neural networks, asset management, condition-based maintenance, machine learning, proactive maintenance, system reliability

© 2020 Pakdad Pourbozorgi Langroudi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Artificial neural networks, asset management, condition-based maintenance, machine learning, proactive maintenance, system reliability