ML- and LSTM-Based Radiator Predictive Maintenance for Energy Saving in Compressed Air Systems

Seung Hyun Jeon, Sarang Yoo, Yoon-Sik Yoo () and Il-Woo Lee
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Seung Hyun Jeon: Department of Computer Engineering, Daejeon University, Daejeon 35235, Republic of Korea
Sarang Yoo: Global Production Operation Team, HD Hyundai Infracore, Incheon 22502, Republic of Korea
Yoon-Sik Yoo: Energy ICT Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea
Il-Woo Lee: Industry & Energy Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea

Energies, 2024, vol. 17, issue 6, 1-12

Abstract: Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%.

Keywords: predictive maintenance; air compressor; machine learning; fault detection; radiator; energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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