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. 2023 Jan 15;23(2):1009.
doi: 10.3390/s23021009.

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Jae Seok Do  1 , Akeem Bayo Kareem  1 , Jang-Wook Hur  1
Affiliations

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Jae Seok Do et al. Sensors (Basel). .

Abstract

Industry 5.0, also known as the "smart factory", is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system's operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

Keywords: anomaly detection; autoencoder; automatic storage and retrieval system; deep learning; long short-term memory; signal processing; vibration sensors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The front and side view of a vertical carousel module type of AS/RS system.
Figure 2
Figure 2
LSTM-Autoencoder Architecture.
Figure 3
Figure 3
The LSTM-Autoencoder Model—a flowchart showing the steps from data acquisition to deployment.
Figure 4
Figure 4
The optimal sensor placement result: (a) Pearson–Fisher Information Matrix, (b) Pearson Effective Independence.
Figure 5
Figure 5
Identification of the vibration sensor placement on the vertical carousel storage and retrieval system.
Figure 6
Figure 6
Pictorial view of the vibration sensor placement on the vertical carousel storage and retrieval system.
Figure 7
Figure 7
The healthy time and frequency analysis for the VCSRS vibration data.
Figure 8
Figure 8
The faulty time and frequency analysis for the VCSRS vibration data.
Figure 9
Figure 9
The healthy and faulty comparison using the fast Fourier Transform.
Figure 10
Figure 10
The power spectral density plot from the four sensor vibration datasets.
Figure 11
Figure 11
Training and validation Loss of the LSTM-autoencoder Model.
Figure 12
Figure 12
Visualization for the set threshold with reconstruction error for different classes.
Figure 13
Figure 13
The confusion matrix for the anomaly detection proposed model between the normal and abnormal class.
See this image and copyright information in PMC

References

    1. Nahavandi S. Industry 5.0—A Human-Centric Solution. Sustainability. 2019;11:4371. doi: 10.3390/su11164371. - DOI
    1. Mourtzis D., Angelopoulos J., Panopoulos N. A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies. 2022;15:6276. doi: 10.3390/en15176276. - DOI
    1. Xu X., Lu Y., Vogel-Heuser B., Wang L. Industry 4.0 and Industry 5.0—Inception, conception and perception. J. Manuf. Syst. 2021;61:530–535. doi: 10.1016/j.jmsy.2021.10.006. - DOI
    1. Adel A. Future of industry 5.0 in society: Human-centric solutions, challenges and prospective research areas. J. Cloud Comp. 2022;11:40. doi: 10.1186/s13677-022-00314-5. - DOI - PMC - PubMed
    1. Grabowska S., Saniuk S., Gajdzik B. Industry 5.0: Improving humanization and sustainability of Industry 4.0. Scientometrics. 2022;127:3117–3144. doi: 10.1007/s11192-022-04370-1. - DOI - PMC - PubMed

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