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Volume 23
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10.3390/s23146502
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Article

Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units

by
Elisavet Karapalidou
1,
Nikolaos Alexandris
1,
Efstathios Antoniou
2,
Stavros Vologiannidis
1,*,
John Kalomiros
1 and
Dimitrios Varsamis
1
1
Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
2
Department of Informatics and Electronics Engineering, International Hellenic University, 57400 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(14), 6502; https://doi.org/10.3390/s23146502
Submission received: 10 June 2023 / Revised: 11 July 2023 / Accepted: 17 July 2023 / Published: 18 July 2023
(This article belongs to the Section Fault Diagnosis & Sensors)
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Abstract

The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based on Artificial Intelligence for the purpose of mitigating machine and equipment failures by predicting anomalies during their production process. In this work, a new dataset that was made publicly available, collected from an industrial blower, is presented, analyzed and modeled using a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder. Specifically the right and left mounted ball bearing units were measured during several months of normal operational condition as well as during an encumbered operational state. An anomaly detection model was developed for the purpose of analyzing the operational behavior of the two bearing units. A stacked sparse Long Short-Term Memory Autoencoder was successfully trained on the data obtained from the left unit under normal operating conditions, learning the underlying patterns and statistical connections of the data. The model was evaluated by means of the Mean Squared Error using data from the unit’s encumbered state, as well as using data collected from the right unit. The model performed satisfactorily throughout its evaluation on all collected datasets. Also, the model proved its capability for generalization along with adaptability on assessing the behavior of equipment similar to the one it was trained on.
Keywords: anomaly detection; autoencoder; deep learning; industrial data; long short-term memory; predictive maintenance anomaly detection; autoencoder; deep learning; industrial data; long short-term memory; predictive maintenance

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MDPI and ACS Style

Karapalidou, E.; Alexandris, N.; Antoniou, E.; Vologiannidis, S.; Kalomiros, J.; Varsamis, D. Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units. Sensors 2023, 23, 6502. https://doi.org/10.3390/s23146502

AMA Style

Karapalidou E, Alexandris N, Antoniou E, Vologiannidis S, Kalomiros J, Varsamis D. Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units. Sensors. 2023; 23(14):6502. https://doi.org/10.3390/s23146502

Chicago/Turabian Style

Karapalidou, Elisavet, Nikolaos Alexandris, Efstathios Antoniou, Stavros Vologiannidis, John Kalomiros, and Dimitrios Varsamis. 2023. "Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units" Sensors 23, no. 14: 6502. https://doi.org/10.3390/s23146502

APA Style

Karapalidou, E., Alexandris, N., Antoniou, E., Vologiannidis, S., Kalomiros, J., & Varsamis, D. (2023). Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units. Sensors, 23(14), 6502. https://doi.org/10.3390/s23146502

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