{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:24Z","timestamp":1787029524035,"version":"3.56.0"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"publisher","award":["118C252"],"award-info":[{"award-number":["118C252"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early fault detection and real-time condition monitoring systems have become quite significant for today\u2019s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers\u2019 cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system\u2019s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.<\/jats:p>","DOI":"10.3390\/s22093208","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:45:21Z","timestamp":1650761121000},"page":"3208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2405-3978","authenticated-orcid":false,"given":"\u00d6zg\u00fcr","family":"G\u00fcltekin","sequence":"first","affiliation":[{"name":"Department of Informatics, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"},{"name":"Center of Intelligent Systems Applications and Research (CISAR), Eskisehir Osmangazi University, Eskisehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyup","family":"Cinar","sequence":"additional","affiliation":[{"name":"Center of Intelligent Systems Applications and Research (CISAR), Eskisehir Osmangazi University, Eskisehir 26040, Turkey"},{"name":"Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kemal","family":"\u00d6zkan","sequence":"additional","affiliation":[{"name":"Center of Intelligent Systems Applications and Research (CISAR), Eskisehir Osmangazi University, Eskisehir 26040, Turkey"},{"name":"Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5589-2032","authenticated-orcid":false,"given":"Ahmet","family":"Yaz\u0131c\u0131","sequence":"additional","affiliation":[{"name":"Center of Intelligent Systems Applications and Research (CISAR), Eskisehir Osmangazi University, Eskisehir 26040, Turkey"},{"name":"Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127733","DOI":"10.1016\/j.jclepro.2021.127733","article-title":"Industry 4.0 and Sustainability: Towards Conceptualization and Theory","volume":"312","author":"Beltrami","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.20965\/ijat.2017.p0004","article-title":"\u201cIndustrie 4.0\u201d and Smart Manufacturing-a Review of Research Issues and Application Examples","volume":"11","author":"Thoben","year":"2017","journal-title":"Int. J. Autom. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.psep.2018.06.026","article-title":"Industry 4.0 as Enabler for a Sustainable Development: A Qualitative Assessment of Its Ecological and Social Potential","volume":"118","author":"Stock","year":"2018","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.promfg.2018.02.034","article-title":"Industry 4.0\u2013a Glimpse","volume":"20","author":"Vaidya","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.cogr.2021.06.001","article-title":"Substantial Capabilities of Robotics in Enhancing Industry 4.0 Implementation","volume":"1","author":"Javaid","year":"2021","journal-title":"Cogn. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3146389","article-title":"On Fault Detection and Diagnosis in Robotic Systems","volume":"51","author":"Khalastchi","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s10033-021-00570-7","article-title":"Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review","volume":"34","author":"Zhao","year":"2021","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F., and Madina, P. (2021). Data-Driven Fault Diagnosis for Electric Drives: A Review. Sensors, 21.","DOI":"10.3390\/s21124024"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A Survey of Fault Diagnosis and Fault-Tolerant Techniques\u2014Part I: Fault Diagnosis with Model-Based and Signal-Based Approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.procir.2016.09.013","article-title":"Collaborative Maintenance in Flow-Line Manufacturing Environments: An Industry 4.0 Approach","volume":"55","author":"Sipsas","year":"2016","journal-title":"Procedia Cirp"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.procir.2016.01.129","article-title":"Opportunities of Sustainable Manufacturing in Industry 4.0","volume":"40","author":"Stock","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Celesti, A., Fazio, M., Gal\u00e1n M\u00e1rquez, F., Glikson, A., Mauwa, H., Bagula, A., Celesti, F., and Villari, M. (2019). How to Develop IoT Cloud E-Health Systems Based on FIWARE: A Lesson Learnt. J. Sens. Actuator Netw., 8.","DOI":"10.3390\/jsan8010007"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.agwat.2016.10.020","article-title":"A Software Architecture Based on FIWARE Cloud for Precision Agriculture","volume":"183","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, P., Santana, J.M., Ortega, S., Trujillo, A., Su\u00e1rez, J.P., Dom\u00ednguez, C., Santana, J., and S\u00e1nchez, A. (2016). SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE. Sensors, 16.","DOI":"10.3390\/s16030417"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.jpdc.2018.12.010","article-title":"Performance Evaluation of FIWARE: A Cloud-Based IoT Platform for Smart Cities","volume":"132","author":"Araujo","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TWC.2019.2946140","article-title":"Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","volume":"19","author":"Li","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4962","DOI":"10.1109\/TITS.2020.2984197","article-title":"A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices","volume":"22","author":"Ke","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.jmsy.2021.02.010","article-title":"Digital Twin-Driven Online Anomaly Detection for an Automation System Based on Edge Intelligence","volume":"59","author":"Huang","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shah, S.K., Tariq, Z., Lee, J., and Lee, Y. (2021). Event-Driven Deep Learning for Edge Intelligence (EDL-EI). Sensors, 21.","DOI":"10.3390\/s21186023"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"359","DOI":"10.5194\/jsss-7-359-2018","article-title":"Sensors 4.0\u2013Smart Sensors and Measurement Technology Enable Industry 4.0","volume":"7","author":"Helwig","year":"2018","journal-title":"J. Sens. Sens. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep Learning and Its Applications to Machine Health Monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.compind.2018.11.003","article-title":"Intelligent Fault Diagnosis Method of Planetary Gearboxes Based on Convolution Neural Network and Discrete Wavelet Transform","volume":"106","author":"Chen","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yao, Y., Wang, H., Li, S., Liu, Z., Gui, G., Dan, Y., and Hu, J. (2018). End-to-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Appl. Sci., 8.","DOI":"10.3390\/app8091584"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108518","DOI":"10.1016\/j.measurement.2020.108518","article-title":"Bearing Fault Diagnosis Based on Vibro-Acoustic Data Fusion and 1D-CNN Network","volume":"173","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4803","DOI":"10.1007\/s00521-021-06668-2","article-title":"A Novel Deep Learning Approach for Intelligent Fault Diagnosis Applications Based on Time-Frequency Images","volume":"34","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.jss.2019.02.024","article-title":"The Robot Operating System: Package Reuse and Community Dynamics","volume":"151","author":"Estefo","year":"2019","journal-title":"J. Syst. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A.Y. (2009, January 12\u201317). ROS: An Open-Source Robot Operating System. Proceedings of the ICRA Workshop on Open Source Software, Kobe, Japan.","DOI":"10.1109\/MRA.2010.936956"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/JPROC.2019.2921977","article-title":"Deep Learning With Edge Computing: A Review","volume":"107","author":"Chen","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_31","unstructured":"(2021, December 12). FIWARE Components. Available online: https:\/\/www.fiware.org\/developers\/catalogue\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117055","DOI":"10.1016\/j.eswa.2022.117055","article-title":"Multisensory Data Fusion-Based Deep Learning Approach for Fault Diagnosis of an Industrial Autonomous Transfer Vehicle","volume":"200","author":"Cinar","year":"2022","journal-title":"Expert Syst. 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