{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:17:32Z","timestamp":1776939452804,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62171046"],"award-info":[{"award-number":["62171046"]}]},{"name":"National Natural Science Foundation of China","award":["92367104"],"award-info":[{"award-number":["92367104"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.<\/jats:p>","DOI":"10.3390\/fi16090310","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T11:58:46Z","timestamp":1724759926000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predictive Maintenance Based on Identity Resolution and Transformers in IIoT"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhibo","family":"Qi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"China Academy of Information and Communications Technology, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3545-1122","authenticated-orcid":false,"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1109\/JSYST.2019.2905565","article-title":"Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey","volume":"13","author":"Zhang","year":"2019","journal-title":"IEEE Syst. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"065111","DOI":"10.1088\/1361-6501\/acc11c","article-title":"Fault diagnosis for rotor based on multi-sensor information and progressive strategies","volume":"34","author":"Hu","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"055901","DOI":"10.1088\/1361-6501\/acb000","article-title":"A method for rolling bearing fault diagnosis based on GSC-MDRNN with multi-dimensional input","volume":"34","author":"Wang","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1007\/s40430-023-04129-6","article-title":"ISHM for fault condition detection in rotating machines with deep learning models","volume":"45","author":"Barella","year":"2023","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1007\/s00170-023-11258-8","article-title":"Misalignment detection on linear feed axis using sensorless motor current signals","volume":"126","author":"Demetgul","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e7886","DOI":"10.1002\/cpe.7886","article-title":"Fault diagnosis for wind turbines based on LSTM and feature optimization strategies","volume":"36","author":"Feng","year":"2024","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111761","DOI":"10.1016\/j.asoc.2024.111761","article-title":"Fault diagnosis method via one vs. rest evidence classifier considering imprecise feature samples","volume":"161","author":"Xu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_9","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_10","first-page":"1877","article-title":"Language Models are Few-Shot Learners","volume":"Volume 33","author":"Brown","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_11","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"Volume 35","author":"Ouyang","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1109\/JIOT.2021.3050441","article-title":"IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning","volume":"10","author":"Teoh","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111506","DOI":"10.1016\/j.asoc.2024.111506","article-title":"A rolling bearing fault diagnosis technique based on recurrence quantification analysis and Bayesian optimization SVM","volume":"156","author":"Wang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119335","DOI":"10.1016\/j.eswa.2022.119335","article-title":"A hybrid multi-stage methodology for remaining useful life prediction of control system: Subsea Christmas tree as a case study","volume":"215","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, Y., Huang, Q., and Zhou, Y. (2023). Intelligent Fault Prognosis Method Based on Stacked Autoencoder and Continuous Deep Belief Network. Actuators, 12.","DOI":"10.3390\/act12030117"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1007\/s13042-023-01807-8","article-title":"Prediction of remaining useful life of rolling element bearings based on LSTM and exponential model","volume":"14","author":"Liu","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22938","DOI":"10.1109\/JIOT.2024.3363610","article-title":"A Remaining Useful Life Prediction Method of Rolling Bearings Based on Deep Reinforcement Learning","volume":"11","author":"Zheng","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108813","DOI":"10.1016\/j.engappai.2024.108813","article-title":"Remaining useful life prediction of machinery based on improved Sample Convolution and Interaction Network","volume":"135","author":"Cen","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111649","DOI":"10.1016\/j.asoc.2024.111649","article-title":"Quantile regression network-based cross-domain prediction model for rolling bearing remaining useful life","volume":"159","author":"Zhang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/COMST.2020.3045136","article-title":"Potential Identity Resolution Systems for the Industrial Internet of Things: A Survey","volume":"23","author":"Ren","year":"2021","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/MCOM.001.2100448","article-title":"A Blockchain-Enabled Trusted Identifier Co-Governance Architecture for the Industrial Internet of Things","volume":"60","author":"Huo","year":"2022","journal-title":"IEEE Commun. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1007\/978-3-030-30484-3_28","article-title":"Local Normalization Based BN Layer Pruning","volume":"Volume 11728","author":"Tetko","year":"2019","journal-title":"Artificial Neural Networks and Machine Learning-ICANN 2019: DEEP LEARNING, PT II, Lecture Notes in Computer Science, Proceedings of the 28th International Conference on Artificial Neural Networks (ICANN), Tech Univ Munchen, Klinikum Rechts Isar, Munich, Germany, 17\u201319 September 2019"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108732","DOI":"10.1016\/j.ymssp.2021.108732","article-title":"Towards better benchmarking using the CWRU bearing fault dataset","volume":"169","author":"Hendriks","year":"2022","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/9\/310\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:43:48Z","timestamp":1760111028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/9\/310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,27]]},"references-count":24,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["fi16090310"],"URL":"https:\/\/doi.org\/10.3390\/fi16090310","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,27]]}}}