{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:32:53Z","timestamp":1774369973089,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,7]],"date-time":"2022-08-07T00:00:00Z","timestamp":1659830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010877","name":"the Shenzhen Science and Technology Innovation Commission (SZSTI)","doi-asserted-by":"publisher","award":["JCYJ20190808181803703"],"award-info":[{"award-number":["JCYJ20190808181803703"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.<\/jats:p>","DOI":"10.3390\/s22155901","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"5901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhi","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Fei","family":"Fei","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China"}]},{"given":"Guanglie","family":"Zhang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong Shenzhen Research Institute, Shenzhen 518057, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.eng.2020.07.017","article-title":"Smart Manufacturing and Intelligent Manufacturing: A Comparative Review","volume":"7","author":"Wang","year":"2021","journal-title":"Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/J.ENG.2017.05.015","article-title":"Intelligent Manufacturing in the Context of Industry 4.0: A Review","volume":"3","author":"Zhong","year":"2017","journal-title":"Engineering"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"W\u00f3jcicki, K., Biega\u0144ska, M., Paliwoda, B., and G\u00f3rna, J. 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