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
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...
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6502 |
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| author | Elisavet Karapalidou Nikolaos Alexandris Efstathios Antoniou Stavros Vologiannidis John Kalomiros Dimitrios Varsamis |
| author_facet | Elisavet Karapalidou Nikolaos Alexandris Efstathios Antoniou Stavros Vologiannidis John Kalomiros Dimitrios Varsamis |
| author_sort | Elisavet Karapalidou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:39:51Z |
| format | Article |
| id | doaj.art-377d153e1fd544dbbe281d7462cf538c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:39:51Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-377d153e1fd544dbbe281d7462cf538c2023-11-18T21:18:39ZengMDPI AGSensors1424-82202023-07-012314650210.3390/s23146502Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing UnitsElisavet Karapalidou0Nikolaos Alexandris1Efstathios Antoniou2Stavros Vologiannidis3John Kalomiros4Dimitrios Varsamis5Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, GreeceDepartment of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, GreeceDepartment of Informatics and Electronics Engineering, International Hellenic University, 57400 Thessaloniki, GreeceDepartment of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, GreeceDepartment of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, GreeceDepartment of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, GreeceThe 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.https://www.mdpi.com/1424-8220/23/14/6502anomaly detectionautoencoderdeep learningindustrial datalong short-term memorypredictive maintenance |
| spellingShingle | Elisavet Karapalidou Nikolaos Alexandris Efstathios Antoniou Stavros Vologiannidis John Kalomiros Dimitrios Varsamis 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 anomaly detection autoencoder deep learning industrial data long short-term memory predictive maintenance |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | anomaly detection autoencoder deep learning industrial data long short-term memory predictive maintenance |
| url | https://www.mdpi.com/1424-8220/23/14/6502 |
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