{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:26:14Z","timestamp":1782314774626,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation project","award":["51209167"],"award-info":[{"award-number":["51209167"]}]},{"name":"National Natural Science Foundation project","award":["12002251"],"award-info":[{"award-number":["12002251"]}]},{"name":"National Natural Science Foundation project","award":["22JC043"],"award-info":[{"award-number":["22JC043"]}]},{"name":"Shaanxi Provincial Department of Education Service Local Special Plan Project","award":["51209167"],"award-info":[{"award-number":["51209167"]}]},{"name":"Shaanxi Provincial Department of Education Service Local Special Plan Project","award":["12002251"],"award-info":[{"award-number":["12002251"]}]},{"name":"Shaanxi Provincial Department of Education Service Local Special Plan Project","award":["22JC043"],"award-info":[{"award-number":["22JC043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classifying the flow subsequences of sensor networks is an effective way for fault detection in the Industrial Internet of Things (IIoT). Traditional fault detection algorithms identify exceptions by a single abnormal dataset and do not pay attention to the factors such as electromagnetic interference, network delay, sensor sample delay, and so on. This paper focuses on fault detection by continuous abnormal points. We proposed a fault detection algorithm within the module of sequence state generated by unsupervised learning (SSGBUL) and the module of integrated encoding sequence classification (IESC). Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the different network cards in the IIoT gateway, and then combined the multiple code sequences into one integrated sequence. Next, we classified the integrated sequence by comparing the integrated sequence with the encoding fault type. The results obtained from the three IIoT datasets of a sewage treatment plant show that the accuracy of the SSGBUL\u2013IESC algorithm exceeds 90% with subsequence length 10, which is significantly higher than the accuracies of the dynamic time warping (DTW) algorithm and the time series forest (TSF) algorithm. The proposed algorithm reaches the classification requirements for fault detection for the IIoT.<\/jats:p>","DOI":"10.3390\/s24072210","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:32:56Z","timestamp":1711891976000},"page":"2210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case"],"prefix":"10.3390","volume":"24","author":[{"given":"Dongfeng","family":"Lei","sequence":"first","affiliation":[{"name":"College of Information and Control Engineering, Xi\u2019an University of Architecture and Technology, Xi\u2019an 710055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Xi\u2019an University of Architecture and Technology, Xi\u2019an 710055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dengfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Xi\u2019an University of Architecture and Technology, Xi\u2019an 710055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/COMST.2022.3141490","article-title":"A comprehensive survey on blockchain in industrial internet of things: Motivations, research progresses, and future challenges","volume":"24","author":"Huo","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100822","DOI":"10.1016\/j.measen.2023.100822","article-title":"Machine learning and internet of things in industry 4.0: A review","volume":"28","author":"Rahman","year":"2023","journal-title":"Meas. Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2140008","DOI":"10.1142\/S0219649221400086","article-title":"Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet","volume":"20","author":"Kannamma","year":"2021","journal-title":"J. Inf. Knowl. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102598","DOI":"10.1016\/j.jnca.2020.102598","article-title":"Hybrid End-to-End VPN Security Approach for Smart IoT Objects","volume":"158","author":"Juma","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9310","DOI":"10.1109\/JIOT.2021.3130434","article-title":"Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System","volume":"9","author":"Zhou","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pasyuk, A., Semenov, E., and Tyuhtyaev, D. (2019, January 1\u20134). Feature Selection in the Classification of Network Traffic Flows. Proceedings of the 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), Vladivostok, Russia.","DOI":"10.1109\/FarEastCon.2019.8934169"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Santo, Y., Immich, R., Dalmazo, B.L., and Riker, A. (2023). Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things. Sensors, 23.","DOI":"10.3390\/s23073544"},{"key":"ref_8","first-page":"25101042","article-title":"Industrial Internet of Things embedded devices fault detection and classification. A case study","volume":"25","author":"Rodrigues","year":"2024","journal-title":"Internet Things"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jammalamadaka, S.K., Chokara, B., Jammalamadaka, S.B., Duvvuri, B.K., and Budaraju, R. (2023). Enhancing the Fault Tolerance of a Multi-Layered IoT Network through Rectangular and Interstitial Mesh in the Gateway Layer. J. Sens. Actuator Netw., 12.","DOI":"10.20944\/preprints202308.0642.v1"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wang, C., and Wang, Q. (2023). Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems. Appl. Sci., 13.","DOI":"10.3390\/app13095380"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","article-title":"The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances","volume":"31","author":"Bagnall","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","article-title":"A time series forest for classification and feature extraction","volume":"239","author":"Deng","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3182382","article-title":"Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles","volume":"12","author":"Lines","year":"2018","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Middlehurst, M., Large, J., and Bagnall, A. (2020, January 10\u201313). The canonical interval forest (CIF) classifier for time series classification. Proceedings of the 2020 IEEE International Conference on Big Data, Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378424"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10844-012-0196-5","article-title":"Rotation-invariant similarity in time series using bag-of-patterns representation","volume":"39","author":"Lin","year":"2012","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106171","DOI":"10.1016\/j.neunet.2024.106171","article-title":"Methodology based on spiking neural networks for univariate time-series forecasting","volume":"173","author":"Lucas","year":"2024","journal-title":"Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time series classification from scratch with deep neural networks: A strong baseline. Proceedings of the 2017 International Joint Conference on Neural Networks, Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl.Discov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100648","DOI":"10.1016\/j.iot.2022.100648","article-title":"A traffic data interpolation method for IoT sensors based on spatio-temporal dependence","volume":"21","author":"Cai","year":"2023","journal-title":"Internet Things"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bandy, A.D., Spyridis, Y., Villarini, B., and Argyriou, V. (2023). Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma. Sensors, 23.","DOI":"10.3390\/s23020926"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zaheer, S., Anjum, N., Hussain, S., Algarni, A.D., Iqbal, J., Bourouis, S., and Ullah, S.S. (2023). A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model. Mathematics, 11.","DOI":"10.3390\/math11030590"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8852","DOI":"10.1109\/JIOT.2020.2996425","article-title":"An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic","volume":"7","author":"Saharkhizan","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13849","DOI":"10.1007\/s00521-022-07175-8","article-title":"Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction","volume":"34","author":"Cheng","year":"2022","journal-title":"Neural Comput.Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3390\/iot4030016","article-title":"Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks","volume":"4","author":"Alaghbari","year":"2023","journal-title":"IoT"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6265","DOI":"10.3233\/JIFS-220230","article-title":"Enabling internet of things in road traffic forecasting with deep learning models","volume":"43","author":"Kumar","year":"2022","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_27","first-page":"561","article-title":"Time series prediction model mWDLNet based on wavelet decomposition and its application research. Small scale microcomputer system","volume":"43","author":"Zhao","year":"2022","journal-title":"J. Chin. Comput. Syst."},{"key":"ref_28","first-page":"224","article-title":"Unsupervised detection method for RoQ covert attacks based on multi-level features","volume":"43","author":"Zhao","year":"2022","journal-title":"J. Commun."},{"key":"ref_29","first-page":"21200312","article-title":"Big data intelligent tourism management platform design based on abnormal behavior identification","volume":"21","author":"Jingyi","year":"2024","journal-title":"Intell. Syst. Appl."},{"key":"ref_30","first-page":"86","article-title":"A Time Series Symbolic Aggregation Approximation Method for Fusion of Trend Information","volume":"40","author":"Huang","year":"2023","journal-title":"Comput. Appl. Res."},{"key":"ref_31","first-page":"88","article-title":"Time series semantic mining algorithm based on subsequence similarity","volume":"48","author":"Lu","year":"2022","journal-title":"Comput. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121668","DOI":"10.1016\/j.eswa.2023.121668","article-title":"Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran","volume":"237","author":"Mahsa","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_33","first-page":"25101082","article-title":"Systematic literature review on mechanisms to measure the technological maturity of the Internet of Things in enterprises","volume":"25","author":"Pino","year":"2024","journal-title":"Internet Things"},{"key":"ref_34","unstructured":"(2021). I\/O Module Enables Remote Data Capture. NASA Tech. Briefs, 45, 49."},{"key":"ref_35","unstructured":"(2020). Banner Engineering Corp. Modbus System Having Actual and Virtual Slave Addresses and Slave Sensors. (10,805,262), U.S. Patent."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"St\u00f3j, J., Kampen, A.-L., Cupek, R., Smo\u0142ka, I., and Drewniak, M. (2023). Industrial Shared Wireless Communication Systems\u2014Use Case of Autonomous Guided Vehicles with Collaborative Robot. Sensors, 23.","DOI":"10.3390\/s23010158"},{"key":"ref_37","first-page":"93681212122","article-title":"Use of Modbus RTU, Profibus DP and HTTP Protocol for AC Motor Control","volume":"12","author":"Miloch","year":"2022","journal-title":"Int. J. Innov. Technol. Explor. Eng. (IJITEE)"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/2569\/1\/012013","article-title":"Design of PROFINET I\/O Real-time Communication System between PLC Based on S7-1200","volume":"2569","author":"Chen","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tiago, M., and Garcia, V.S.O. (2022). Enhanced Modbus\/TCP Security Protocol: Authentication and Authorization Functions Supported. Sensors, 22.","DOI":"10.3390\/s22208024"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"172106106","DOI":"10.1016\/j.neunet.2024.106106","article-title":"Self-supervised anomaly detection in computer vision and beyond: A survey and outlook","volume":"172","author":"Hojjati","year":"2024","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101025","DOI":"10.1016\/j.iot.2023.101025","article-title":"IoT data dissemination scheme for reducing delay in multi-broker environments","volume":"25","author":"Cho","year":"2024","journal-title":"Internet Things"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Comte, A., Tricou, T., Tannier, E., Joseph, J., Siberchicot, A., Penel, S., Allio, R., Delsuc, F., Dray, S., and de Vienne, D.M. (2023). PhylteR: Efficient identification of outlier sequences in phylogenomic datasets. Mol. Biol. Evol., 40.","DOI":"10.1093\/molbev\/msad234"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2210\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:21:08Z","timestamp":1760106068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":42,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072210"],"URL":"https:\/\/doi.org\/10.3390\/s24072210","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]}}}