Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

Kang, Jaeyong; Kim, Chul-Su; Kang, Jeong Won; Gwak, Jeonghwan

doi:10.3390/app11199290

Cited by 26 publications

(13 citation statements)

References 35 publications

(36 reference statements)

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“…In this paper, we use the sample data of web application transgression scenarios and train the LSTM-AutoEncoder model to identify the pages of transgression scenarios, which solves the problem of false positives in traditional transgression detection and improves the accuracy of transgression detection. The experimental data show that the LSTM-AutoEncoder model has certain accuracy advantages over the traditional one-class SVM model and AutoEncoder model under certain amount of data scale and also has great advantages in processing web text sequences with contextual relationships, which is as follows [ 31 ]: In the one-class model, the model reaches a very high precision of 0.974 and has a high recognition rate for unauthorized scene pages in the dataset. However, the recall rate of the model is only 0.473, which makes the non-unauthorized scene web page be recognized by the model as an unauthorized scene page.…”

Section: Discussionmentioning

confidence: 99%

“…LSTM-AutoEncoder model has certain accuracy advantages over the traditional one-class SVM model and AutoEncoder model under certain amount of data scale and also has great advantages in processing web text sequences with contextual relationships, which is as follows [31]: (1) e application of AI and network security is at a relatively early stage of development, and this paper provides a good application case. However, the experimental data in this paper also have certain shortcomings.…”

Section: Data Availabilitymentioning

confidence: 98%

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The Application of Computer Intelligence in the Cyber-Physical Business System Integration in Network Security

Shi

Lü

et al. 2022

Computational Intelligence and Neuroscience

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In order to address the false alarm detection problem caused by the inability to identify the transgression scene pages in the process of horizontal transgression detection, this study proposes a deep learning-based LSTM-AutoEncoder unsupervised prediction model. The model uses long short-term memory network to build AutoEncoder, extracts text features of page response data of horizontal transgression scenario, and reconstructs text features to restore. Meanwhile, it counts the error between the restored result and the original page response, judges whether the detection result of horizontal transgression is false alarm according to the error threshold of unknown page, and tests the effectiveness of the model effect under real business data by comparing it with other two algorithms, one-class SVM and AutoEncoder, which provides security for enterprise network business. The results show that the LSTM-AutoEncoder model achieves a more balanced index in terms of accuracy, precision, recall, and F1-score in the case of MAE, which is 0.3% more and 0.2% more than the case of MSE in terms of recall and accuracy. It is concluded that the LSTM-AutoEncoder model is more in line with the real business requirements, and the simple model architecture selected for this study can reduce the complexity of the model, speed up the prediction time of the model in the application phase, and improve the performance of the detection software. This indicates that this study has some application prospects in network security.

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Section: Discussionmentioning

confidence: 99%

Section: Data Availabilitymentioning

confidence: 98%

The Application of Computer Intelligence in the Cyber-Physical Business System Integration in Network Security

Shi

Lü

et al. 2022

Computational Intelligence and Neuroscience

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“…An unlabeled dataset can be framed as a supervised learning problem to produce an output

that represents the original input

. This network can be trained by decreasing the reconstruction error (x,

), which gauges the discrepancies between an initial information (input sequence) and the resulting reconstruction sequence [ 51 , 52 , 53 ].…”

Section: Theoretical Backgroundsmentioning

confidence: 99%

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

Seok

Kareem

Hur

2023

Sensors

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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.

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“…Anomaly detection using an autoencoder learns important characteristics of normal samples by learning under the assumption that most of the data used for training are normal samples. In addition, the loss function-based threshold is calculated [ 52 , 53 , 54 , 55 ], and if there is a loss function value that exceeds the threshold, the corresponding data are determined as abnormal data:

where

is input data and

is reconstruct input data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic

Jang

Kim

Lee

et al. 2022

IJERPH

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Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this severe situation. Accordingly, there has also been an increase in research related to a decision support system based on simulation approaches used as a basis for PHSMs. However, previous studies showed limitations impeding utilization as a decision support system for policy establishment and implementation, such as the failure to reflect changes in the effectiveness of PHSMs and the restriction to short-term forecasts. Therefore, this study proposes an LSTM-Autoencoder-based decision support system for establishing and implementing PHSMs. To overcome the limitations of existing studies, the proposed decision support system used a methodology for predicting the number of daily confirmed cases over multiple periods based on multiple output strategies and a methodology for rapidly identifying varies in policy effects based on anomaly detection. It was confirmed that the proposed decision support system demonstrated excellent performance compared to models used for time series analysis such as statistical models and deep learning models. In addition, we endeavored to increase the usability of the proposed decision support system by suggesting a transfer learning-based methodology that can efficiently reflect variations in policy effects. Finally, the decision support system proposed in this study provides a methodology that provides multi-period forecasts, identifying variations in policy effects, and efficiently reflects the effects of variation policies. It was intended to provide reasonable and realistic information for the establishment and implementation of PHSMs and, through this, to yield information expected to be highly useful, which had not been provided in the decision support systems presented in previous studies.

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Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

Cited by 26 publications

References 35 publications

The Application of Computer Intelligence in the Cyber-Physical Business System Integration in Network Security

The Application of Computer Intelligence in the Cyber-Physical Business System Integration in Network Security

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

A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic

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