{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T07:55:44Z","timestamp":1725436544027},"reference-count":45,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"e-Mobility R&D Research Program through the Gangwon Technopark (GWTP) funded by Gangwon Province","award":["GWTP 2022-397"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most current detection methods rely on prior knowledge, it is difficult to identify new or previously unknown anomalies. In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. First, we train our model on EPS data by employing an autoencoder to extract features and compress them into a latent representation. The compressed features are fed into the LSTM network to capture any correlated dependencies between features, which are then reconstructed as output. An anomaly score is used to detect anomalies based on the reconstruction loss of the output. The effectiveness of our proposed approach is demonstrated by collecting sample data from an experiment using an EPS test jig. The comparison results indicate that our proposed model performs better in detecting anomalies, with an accuracy of 0.99 and a higher area under the receiver operating characteristic curve than other methods providing a valuable tool for anomaly detection in EPS.<\/jats:p>","DOI":"10.3390\/s22228981","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T09:39:59Z","timestamp":1669023599000},"page":"8981","source":"Crossref","is-referenced-by-count":8,"title":["A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System"],"prefix":"10.3390","volume":"22","author":[{"given":"Lawal Wale","family":"Alabe","sequence":"first","affiliation":[{"name":"Department of AI Convergence, Pukyong National University, Nam-gu, Busan 48513, Republic of Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6236-1379","authenticated-orcid":false,"given":"Kimleang","family":"Kea","sequence":"additional","affiliation":[{"name":"Department of AI Convergence, Pukyong National University, Nam-gu, Busan 48513, Republic of Korea"}]},{"given":"Youngsun","family":"Han","sequence":"additional","affiliation":[{"name":"Department of AI Convergence, Pukyong National University, Nam-gu, Busan 48513, Republic of Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0334-5560","authenticated-orcid":false,"given":"Young Jae","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Electric and Electronic Engineering, Halla University, Wonju 26404, Republic of Korea"}]},{"given":"Taekyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Technology, Incheon Jaeneung University, Dong-gu, Incheon 22573, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"ref_1","unstructured":"Xiaoling, W., Yan, Z., and Hong, W. 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