Steam Trap Maintenance-Prioritizing Model Based on Big Data

Jiwon Roh, Subean Jang, Suyeun Kim, Hyungtae Cho, Junghwan Kim

Research output: Contribution to journalArticlepeer-review


Steam traps in large facilities need continuous maintenance to prevent corrosion and other damage that could pose a considerable threat to a facility and its workers. However, a significant amount of human resources is required for the maintenance of steam traps. An automatic method to inform stakeholders regarding maintenance cycles will be beneficial for the maintenance process. Therefore, an optimal maintenance priority decision model is developed in this study to establish an efficient steam trap management system. First, the frequency of failures, installation locations, and specifications of steam traps were determined as parameters causing a failure. A relative score and conversion score are calculated for each parameter. The final conversion score is the sum of the conversion score multiplied by the corresponding steam trap data weight factor. Steam traps within the range requiring inspection are classified as high priority cases. Experimental results confirmed that the failure accuracy rate is approximately 95%, and the average failure error rate is within 3%. Additionally, the number of steam traps to be checked was reduced by 3616. The proposed model significantly reduces maintenance in commercial industries.

Original languageEnglish
Pages (from-to)4408-4416
Number of pages9
JournalACS Omega
Issue number6
Publication statusPublished - 2021 Feb 16

Bibliographical note

Publisher Copyright:
© 2021 The Authors. Published by American Chemical Society.

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)


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