{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:43:12Z","timestamp":1770885792685,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,18]],"date-time":"2024-08-18T00:00:00Z","timestamp":1723939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB4000505"],"award-info":[{"award-number":["2021YFB4000505"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Abnormal valve positions can lead to fluctuations in the process industry, potentially triggering serious accidents. For processes that frequently require operational switching, such as green chemical processes based on renewable energy or biotechnological fermentation processes, this issue becomes even more severe. Despite this risk, many plants still rely on manual inspections to check valve status. The widespread use of cameras in large plants now makes it feasible to monitor valve positions through computer vision technology. This paper proposes a novel real-time valve monitoring approach based on computer vision to detect abnormalities in valve positions. Utilizing an improved network architecture based on YOLO V8, the method performs valve detection and feature recognition. To address the challenge of small, relatively fixed-position valves in the images, a coord attention module is introduced, embedding position information into the feature channels and enhancing the accuracy of valve rotation feature extraction. The valve position is then calculated using a rotation algorithm with the valve\u2019s center point and bounding box coordinates, triggering an alarm for valves that exceed a pre-set threshold. The accuracy and generalization ability of the proposed approach are evaluated through experiments on three different types of valves in two industrial scenarios. The results demonstrate that the method meets the accuracy and robustness standards required for real-time valve monitoring in industrial applications.<\/jats:p>","DOI":"10.3390\/s24165337","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:41:31Z","timestamp":1724049691000},"page":"5337","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Real-Time Intelligent Valve Monitoring Approach through Cameras Based on Computer Vision Methods"],"prefix":"10.3390","volume":"24","author":[{"given":"Zihui","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Chemical Engineering, Sichuan University, Chengdu 610065, China"}]},{"given":"Qiyuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering, Sichuan University, Chengdu 610065, China"}]},{"given":"Heping","family":"Jin","sequence":"additional","affiliation":[{"name":"China Three Gorges Corporation, Beijing 100038, China"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"China Three Gorges Corporation, Beijing 100038, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6000-9160","authenticated-orcid":false,"given":"Yiyang","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s41872-022-00201-7","article-title":"Safety and reliability improvement of valves and actuators in the offshore oil and gas industry","volume":"11","author":"Sotoodeh","year":"2022","journal-title":"Life Cycle Reliab. Saf. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.psep.2022.01.028","article-title":"Optimal emergency shutdown valve configuration for pressurised pipelines","volume":"159","author":"Yu","year":"2022","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104788","DOI":"10.1016\/j.ssci.2020.104788","article-title":"Characteristics of hazardous chemical accidents during hot season in China from 1989 to 2019: A statistical investigation","volume":"129","author":"Wang","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Megaraj, M., Dillibabu, S.P., Durvasulu, R., Manjunathan, K., Palanivel, A., Vasudevan, B., and Grace, N. (2023). Post lockdown industrial accidents and their safety ontology. AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/5.0139346"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.cherd.2023.06.024","article-title":"A novel multi-view enhanced visual detection for cavitation of control valve","volume":"195","author":"Sun","year":"2023","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6114","DOI":"10.1002\/cjce.25054","article-title":"Control valve stiction detection using Markov transition field and deep convolutional neural network","volume":"101","author":"Memarian","year":"2023","journal-title":"Can. J. Chem. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3963","DOI":"10.1002\/cjce.24767","article-title":"A convolutional neural network (CNN)-based direct method to detect stiction in control valves","volume":"101","author":"Akavalappil","year":"2023","journal-title":"Can. J. Chem. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1016\/j.psep.2021.01.050","article-title":"Detection of pressure relief valve leakage by tuning generated sound characteristics","volume":"148","author":"Cao","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.psep.2023.01.027","article-title":"Multi-leakage source localization of safety valve based on improved KDE algorithm","volume":"171","author":"Hou","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.psep.2023.08.071","article-title":"Improved machine learning leak fault recognition for low-pressure natural gas valve","volume":"178","author":"Liu","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106675","DOI":"10.1016\/j.ymssp.2020.106675","article-title":"Multivariable modeling of valve inner leakage acoustic emission signal based on Gaussian process","volume":"140","author":"Ye","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","first-page":"28","article-title":"Precision in valve position indication","volume":"46","author":"Peyvan","year":"2001","journal-title":"Nucl. Eng. Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7507","DOI":"10.1021\/ie4031264","article-title":"Stiction Quantification: A Robust Methodology for Valve Monitoring and Maintenance Scheduling","volume":"53","author":"Scali","year":"2014","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.2118\/218017-PA","article-title":"A System to Detect Oilwell Anomalies Using Deep Learning and Decision Diagram Dual Approach","volume":"29","author":"Aranha","year":"2024","journal-title":"SPE J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2300130","DOI":"10.1002\/adsr.202300130","article-title":"Sensor Technologies for Hydraulic Valve and System Performance Monitoring: Challenges and Perspectives","volume":"3","author":"Liu","year":"2024","journal-title":"Adv. Sens. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1002\/aic.16489","article-title":"The promise of artificial intelligence in chemical engineering: Is it here, finally?","volume":"65","author":"Venkatasubramanian","year":"2019","journal-title":"Aiche J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e18475","DOI":"10.1002\/aic.18475","article-title":"Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?","volume":"70","author":"Arunthavanathan","year":"2024","journal-title":"Aiche J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.coche.2016.07.009","article-title":"Abnormal situation management for smart chemical process operation","volume":"14","author":"Dai","year":"2016","journal-title":"Curr. Opin. Chem. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.psep.2019.05.016","article-title":"An intelligent fire detection approach through cameras based on computer vision methods","volume":"127","author":"Wu","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.psep.2022.06.037","article-title":"A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method","volume":"164","author":"Huang","year":"2022","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mun, J., Kim, J., Do, Y., Kim, H., Lee, C., and Jeong, J. (2023). Design and Implementation of Defect Detection System Based on YOLOv5-CBAM for Lead Tabs in Secondary Battery Manufacturing. Processes, 11.","DOI":"10.3390\/pr11092751"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.psep.2024.04.123","article-title":"A computer-vision-based deep learning model of smoke diffusion","volume":"187","author":"Zhou","year":"2024","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"122689","DOI":"10.1016\/j.eswa.2023.122689","article-title":"A study of engine room smoke detection based on proactive machine vision model for intelligent ship","volume":"241","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, J., Chen, L., and Zhang, Y. (2024). Improving Computer Vision-Based Wildfire Smoke Detection by Combining SE-ResNet with SVM. Processes, 12.","DOI":"10.3390\/pr12040747"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.psep.2022.09.002","article-title":"Underwater gas leak detection using an autonomous underwater vehicle (robotic fish)","volume":"167","author":"Hu","year":"2022","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106780","DOI":"10.1016\/j.compchemeng.2020.106780","article-title":"Real-time leak detection using an infrared camera and Faster R-CNN technique","volume":"135","author":"Shi","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.psep.2020.11.053","article-title":"Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network","volume":"146","author":"Song","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"26247","DOI":"10.1109\/ACCESS.2023.3257183","article-title":"Safety Helmet Wearing Detection Model Based on Improved YOLO-M","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.compind.2018.03.037","article-title":"An intelligent vision-based approach for helmet identification for work safety","volume":"100","author":"Wu","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hayat, A., and Morgado-Dias, F. (2022). Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety. Appl. Sci., 12.","DOI":"10.3390\/app12168268"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111410","DOI":"10.1016\/j.measurement.2022.111410","article-title":"Visual measurement of valve opening area with improved subpixel edge location","volume":"198","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5501010","DOI":"10.1109\/TIM.2021.3130292","article-title":"Continuous Status Monitoring of Industrial Valve Using OCC-Enabled Wireless Sensor Network","volume":"71","author":"Ahmed","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","first-page":"650","article-title":"Research and application of cone valve seal detection algorithm based on yolov3","volume":"12462","author":"Xu","year":"2023","journal-title":"Proc. SPIE"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qin, R., Hua, Z., Sun, Z., and He, R. (2022). Recognition method of knob gear in substation based on YOLOv4 and Darknet53-DUC-DSNT. Sensors, 22.","DOI":"10.3390\/s22134722"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"60015","DOI":"10.1109\/ACCESS.2024.3394066","article-title":"Substation High-Voltage Switchgear Detection Based on Improved EfficientNet-YOLOv5s Model","volume":"12","author":"Sun","year":"2024","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, Z., Tohti, G., Geni, M., He, H., and Turhun, F. (2024). Wind turbine rotor blade encoding marker recognition method based on improved YOLOv8 model. Signal Image Video Process., 1\u201312.","DOI":"10.1007\/s11760-024-03365-0"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TIP.2023.3348697","article-title":"Probabilistic Intersection-Over-Union for Training and Evaluation of Oriented Object Detectors","volume":"33","author":"Kirsten","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","first-page":"21002","article-title":"Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection","volume":"33","author":"Li","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5337\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:38:26Z","timestamp":1760110706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5337"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,18]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165337"],"URL":"https:\/\/doi.org\/10.3390\/s24165337","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,18]]}}}