A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment

Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and to...

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Veröffentlicht in:Electronics (Basel) 2024-05, Vol.13 (9), p.1700
Hauptverfasser: Lee, Seonwoo, Kareem, Akeem Bayo, Hur, Jang-Wook
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creator Lee, Seonwoo
Kareem, Akeem Bayo
Hur, Jang-Wook
description Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques.
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source MDPI - Multidisciplinary Digital Publishing Institute; Free E-Journal (出版社公開部分のみ)
subjects Algorithms
Anomalies
Case studies
Comparative analysis
Comparative studies
Cybersecurity
Data acquisition
Data management
Data storage
Deep learning
Efficiency
Electric motors
Energy consumption
Industrial engineering
Industry 4.0
Information storage and retrieval
Machinery
Manufacturing
Performance evaluation
Preventive maintenance
Productivity
Rotating machinery
Sensors
Signal processing
Time series
Vibration
title A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment
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