{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:16:52Z","timestamp":1791915412249,"version":"4.3.4"},"reference-count":27,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Accurate and real-time monitoring of motor operating states is essential for ensuring the reliability, safety, and efficiency of modern industrial systems. This paper presents a multi-sensor fusion framework for intelligent motor condition monitoring, which integrates wavelet-based feature extraction, shallow neural network classification, and evidence-theoretic decision fusion. A compact hardware platform is developed to synchronously acquire vibration, acoustic, and magnetic field signals under multiple motor operating conditions. The acquired signals are segmented using sliding windows and decomposed via wavelet packet transform to extract energy distribution features. These features are independently processed by BP neural networks trained on individual sensor modalities, and their softmax outputs are fused through Dempster\u2013Shafer theory to enhance classification robustness and confidence. The proposed system is evaluated on a structured dataset covering three typical motor states: shutdown, no-load, and loaded operation. Experimental results show that the fusion-based model achieves an overall accuracy of 92.8%, outperforming all single-sensor baselines. Additional analyses of confidence distribution, confusion matrix, and ROC curves confirm the superiority of the proposed method in decision reliability and class separability. The developed framework offers a scalable, interpretable, and deployable solution for intelligent motor health monitoring and has strong potential for practical implementation in predictive maintenance applications.<\/jats:p>","DOI":"10.1371\/journal.pone.0335360","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:50:16Z","timestamp":1762541416000},"page":"e0335360","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-time motor operating state recognition via multi-sensor fusion: A wavelet\u2013neural\u2013evidence framework for industrial condition monitoring"],"prefix":"10.1371","volume":"20","author":[{"given":"Gong","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5407-1203","authenticated-orcid":true,"given":"Peng","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"pone.0335360.ref001","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ymssp.2013.06.004","article-title":"Prognostics and health management design for rotary machinery systems\u2014reviews, methodology and applications","volume":"42","author":"J Lee","year":"2014","journal-title":"Mechanical Systems and Signal Processing."},{"key":"pone.0335360.ref002","first-page":"292","article-title":"Intelligent fault diagnosis of rolling bearings using hierarchical convolutional networks","volume":"87","author":"C Lu","year":"2017","journal-title":"Mechanical Systems and Signal Processing."},{"key":"pone.0335360.ref003","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":"R Zhao","year":"2019","journal-title":"Mechanical Systems and Signal Processing."},{"issue":"7","key":"pone.0335360.ref004","first-page":"5690","article-title":"Multi-layer domain adaptation method for rolling bearing fault diagnosis","volume":"67","author":"X Li","year":"2020","journal-title":"IEEE Transactions on Industrial Electronics."},{"key":"pone.0335360.ref005","doi-asserted-by":"crossref","first-page":"114658","DOI":"10.1016\/j.measurement.2024.114658","article-title":"Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: a comprehensive review","volume":"232","author":"F Kibrete","year":"2024","journal-title":"Measurement."},{"issue":"12","key":"pone.0335360.ref006","doi-asserted-by":"crossref","first-page":"4024","DOI":"10.3390\/s21124024","article-title":"Data-driven fault diagnosis for electric drives: a review","volume":"21","author":"D Gonzalez-Jimenez","year":"2021","journal-title":"Sensors (Basel)."},{"key":"pone.0335360.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sigpro.2013.04.015","article-title":"Wavelets for fault diagnosis of rotary machines: a review with applications","volume":"96","author":"R Yan","year":"2014","journal-title":"Signal Processing."},{"issue":"6","key":"pone.0335360.ref008","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.3390\/app11062761","article-title":"Trends and challenges in intelligent condition monitoring of electrical machines using machine learning","volume":"11","author":"K Kudelina","year":"2021","journal-title":"Applied Sciences."},{"issue":"6","key":"pone.0335360.ref009","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1016\/j.ymssp.2006.12.007","article-title":"Support vector machine in machine condition monitoring and fault diagnosis","volume":"21","author":"A Widodo","year":"2007","journal-title":"Mechanical Systems and Signal Processing."},{"key":"pone.0335360.ref010","first-page":"174091","article-title":"Condition monitoring of electrical machines using machine learning techniques","volume":"8","author":"T Wang","year":"2020","journal-title":"IEEE Access."},{"key":"pone.0335360.ref011","first-page":"251","article-title":"A novel hierarchical diagnostic network based on deep transfer learning","volume":"91","author":"W Sun","year":"2019","journal-title":"ISA Transactions."},{"key":"pone.0335360.ref012","first-page":"256","article-title":"Bearing fault diagnosis using ensemble empirical mode decomposition and SVM","volume":"82","author":"H Jiang","year":"2016","journal-title":"Measurement."},{"issue":"6","key":"pone.0335360.ref013","first-page":"1360","article-title":"Motor fault classification using deep belief networks","volume":"67","author":"L Xie","year":"2018","journal-title":"IEEE Transactions on Instrumentation and Measurement."},{"issue":"7","key":"pone.0335360.ref014","first-page":"5290","article-title":"Fault diagnosis of electric motors using STFT and convolutional neural networks","volume":"66","author":"K Zhao","year":"2019","journal-title":"IEEE Transactions on Industrial Electronics."},{"key":"pone.0335360.ref015","first-page":"1","article-title":"Fault diagnosis under noisy conditions using deep convolutional neural networks","volume":"310","author":"C Zhang","year":"2018","journal-title":"Neurocomputing."},{"key":"pone.0335360.ref016","first-page":"153420","article-title":"A 1D CNN-based diagnosis framework for current signals in motor systems","volume":"7","author":"Y Zhang","year":"2019","journal-title":"IEEE Access."},{"key":"pone.0335360.ref017","first-page":"108918","article-title":"Multi-scale and attention-based deep learning for motor anomaly detection","volume":"123","author":"Y He","year":"2022","journal-title":"Applied Soft Computing."},{"issue":"8","key":"pone.0335360.ref018","first-page":"5822","article-title":"A new CNN-LSTM approach for fault diagnosis with noisy data","volume":"69","author":"D Wang","year":"2020","journal-title":"IEEE Transactions on Instrumentation and Measurement."},{"key":"pone.0335360.ref019","first-page":"55162","article-title":"Multi-channel deep learning for motor health monitoring","volume":"7","author":"Z Feng","year":"2019","journal-title":"IEEE Access."},{"issue":"2","key":"pone.0335360.ref020","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/TPWRS.2024.3447533","article-title":"A heterogeneous graph-based multi-task learning for fault event diagnosis in smart grid","volume":"40","author":"D Chanda","year":"2025","journal-title":"IEEE Trans Power Syst."},{"issue":"4","key":"pone.0335360.ref021","first-page":"3521","article-title":"Deep learning for motor fault detection: Benchmark and challenges","volume":"70","author":"Y Liu","year":"2023","journal-title":"IEEE Transactions on Industrial Electronics."},{"key":"pone.0335360.ref022","unstructured":"Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: Proceedings of the International Conference on Learning Representations (ICLR); 2018."},{"key":"pone.0335360.ref023","doi-asserted-by":"crossref","DOI":"10.1515\/9780691214696","volume-title":"A mathematical theory of evidence","author":"G Shafer","year":"1976"},{"key":"pone.0335360.ref024","first-page":"1","article-title":"DS-based fusion strategy for multi-modal fault diagnosis","volume":"57","author":"B Tang","year":"2020","journal-title":"Information Fusion."},{"issue":"7","key":"pone.0335360.ref025","first-page":"6031","article-title":"Attention-augmented deep learning for intelligent fault diagnosis","volume":"68","author":"J Chen","year":"2021","journal-title":"IEEE Transactions on Industrial Electronics."},{"key":"pone.0335360.ref026","doi-asserted-by":"crossref","first-page":"3497","DOI":"10.1109\/ACCESS.2024.3524763","article-title":"Robust spike sorting using dual tree complex wavelet transform: overcoming traditional limitations","volume":"13","author":"G Serbes","year":"2025","journal-title":"IEEE Access."},{"key":"pone.0335360.ref027","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s11517-014-1224-0","article-title":"Directional dual-tree complex wavelet packet transforms for processing quadrature signals","volume":"54","author":"G Serbes","year":"2016","journal-title":"Med Biol Eng Comput."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0335360","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:50:22Z","timestamp":1762541422000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0335360"}},"subtitle":[],"editor":[{"given":"Agbotiname","family":"Lucky Imoize","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":27,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11,7]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0335360","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}