Predictive maintenance of water purification unit for smart factories

Tsung Yuan Chang, Wei Ting Cho, Shau Yin Tseng, Yeni Ouyang, Chin Feng Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In recent years, the applications of the smart factory are very popular. Predictive maintenance is one of the issues. Some research achieved the goal of predictive maintenance with Artificial Intelligence (AI). Here we focus on the local scrubber (LSR) system, a water purification and recycling system. This paper proposed a machine learning model to solve predictive maintenance problem. The device learns the pattern of input data through the RNN model and classify the different state of device. We can know the current situation of the device and judge whether it is about to be replaced. As far as we know, this is the first predictive task maintenance in the LSR system and has an accuracy of 84% in the datasets of different years. The smart factory will come true while the LSR system can be reduce cost, manpower, time and money with predictive maintenance.

Original languageEnglish
Title of host publicationCognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
EditorsJian Shen, Yao-Chung Chang, Yu-Sheng Su, Hiroaki Ogata
Number of pages9
ISBN (Print)9789811561122
Publication statusPublished - 2020
Event2nd International Cognitive Cities Conference, IC3 2019 - Kyoto, Japan
Duration: 2019 Sept 32019 Sept 6

Publication series

NameCommunications in Computer and Information Science
Volume1227 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference2nd International Cognitive Cities Conference, IC3 2019

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics


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