Predictive maintenance of water purification unit for smart factories

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

doi:10.1007/978-981-15-6113-9_8

Predictive maintenance of water purification unit for smart factories

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

Department of Engineering Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
Editors	Jian Shen, Yao-Chung Chang, Yu-Sheng Su, Hiroaki Ogata
Publisher	Springer
Pages	62-70
Number of pages	9
ISBN (Print)	9789811561122
DOIs	https://doi.org/10.1007/978-981-15-6113-9_8
Publication status	Published - 2020
Event	2nd International Cognitive Cities Conference, IC3 2019 - Kyoto, Japan Duration: 2019 Sept 3 → 2019 Sept 6

Publication series

Name	Communications in Computer and Information Science
Volume	1227 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Cognitive Cities Conference, IC3 2019
Country/Territory	Japan
City	Kyoto
Period	19-09-03 → 19-09-06

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-15-6113-9_8

Cite this

Chang, T. Y., Cho, W. T., Tseng, S. Y., Ouyang, Y., & Lai, C. F. (2020). Predictive maintenance of water purification unit for smart factories. In J. Shen, Y.-C. Chang, Y.-S. Su, & H. Ogata (Eds.), Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers (pp. 62-70). (Communications in Computer and Information Science; Vol. 1227 CCIS). Springer. https://doi.org/10.1007/978-981-15-6113-9_8

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title = "Predictive maintenance of water purification unit for smart factories",

abstract = "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.",

author = "Chang, \{Tsung Yuan\} and Cho, \{Wei Ting\} and Tseng, \{Shau Yin\} and Yeni Ouyang and Lai, \{Chin Feng\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2020.; 2nd International Cognitive Cities Conference, IC3 2019 ; Conference date: 03-09-2019 Through 06-09-2019",

year = "2020",

doi = "10.1007/978-981-15-6113-9\_8",

language = "English",

isbn = "9789811561122",

series = "Communications in Computer and Information Science",

publisher = "Springer",

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editor = "Jian Shen and Yao-Chung Chang and Yu-Sheng Su and Hiroaki Ogata",

booktitle = "Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers",

}

Chang, TY, Cho, WT, Tseng, SY, Ouyang, Y & Lai, CF 2020, Predictive maintenance of water purification unit for smart factories. in J Shen, Y-C Chang, Y-S Su & H Ogata (eds), Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1227 CCIS, Springer, pp. 62-70, 2nd International Cognitive Cities Conference, IC3 2019, Kyoto, Japan, 19-09-03. https://doi.org/10.1007/978-981-15-6113-9_8

Predictive maintenance of water purification unit for smart factories. / Chang, Tsung Yuan; Cho, Wei Ting; Tseng, Shau Yin et al.
Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers. ed. / Jian Shen; Yao-Chung Chang; Yu-Sheng Su; Hiroaki Ogata. Springer, 2020. p. 62-70 (Communications in Computer and Information Science; Vol. 1227 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Predictive maintenance of water purification unit for smart factories

AU - Chang, Tsung Yuan

AU - Cho, Wei Ting

AU - Tseng, Shau Yin

AU - Ouyang, Yeni

AU - Lai, Chin Feng

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2020.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85087279675

SN - 9789811561122

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BT - Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers

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PB - Springer

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Y2 - 3 September 2019 through 6 September 2019

ER -

Predictive maintenance of water purification unit for smart factories

Abstract

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UN SDGs

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