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Anomaly Detection Based on Time Series Data of Hydraulic Accumulator
Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of p...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739721/ https://www.ncbi.nlm.nih.gov/pubmed/36502152 http://dx.doi.org/10.3390/s22239428 |
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author | Park, Min-Ho Chakraborty, Sabyasachi Vuong, Quang Dao Noh, Dong-Hyeon Lee, Ji-Woong Lee, Jae-Ung Choi, Jae-Hyuk Lee, Won-Ju |
author_facet | Park, Min-Ho Chakraborty, Sabyasachi Vuong, Quang Dao Noh, Dong-Hyeon Lee, Ji-Woong Lee, Jae-Ung Choi, Jae-Hyuk Lee, Won-Ju |
author_sort | Park, Min-Ho |
collection | PubMed |
description | Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set. |
format | Online Article Text |
id | pubmed-9739721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97397212022-12-11 Anomaly Detection Based on Time Series Data of Hydraulic Accumulator Park, Min-Ho Chakraborty, Sabyasachi Vuong, Quang Dao Noh, Dong-Hyeon Lee, Ji-Woong Lee, Jae-Ung Choi, Jae-Hyuk Lee, Won-Ju Sensors (Basel) Article Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set. MDPI 2022-12-02 /pmc/articles/PMC9739721/ /pubmed/36502152 http://dx.doi.org/10.3390/s22239428 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Park, Min-Ho Chakraborty, Sabyasachi Vuong, Quang Dao Noh, Dong-Hyeon Lee, Ji-Woong Lee, Jae-Ung Choi, Jae-Hyuk Lee, Won-Ju Anomaly Detection Based on Time Series Data of Hydraulic Accumulator |
title | Anomaly Detection Based on Time Series Data of Hydraulic Accumulator |
title_full | Anomaly Detection Based on Time Series Data of Hydraulic Accumulator |
title_fullStr | Anomaly Detection Based on Time Series Data of Hydraulic Accumulator |
title_full_unstemmed | Anomaly Detection Based on Time Series Data of Hydraulic Accumulator |
title_short | Anomaly Detection Based on Time Series Data of Hydraulic Accumulator |
title_sort | anomaly detection based on time series data of hydraulic accumulator |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739721/ https://www.ncbi.nlm.nih.gov/pubmed/36502152 http://dx.doi.org/10.3390/s22239428 |
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