{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:34:42Z","timestamp":1776184482196,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology)","award":["HBIR202107"],"award-info":[{"award-number":["HBIR202107"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A radar is an important part of an air defense and combat system. It is of great significance to military defense to improve the effectiveness of radar state monitoring and the accuracy of fault diagnosis during operation. However, the complexity of radar equipment\u2019s structure and the uncertainty of the operating environment greatly increase the difficulty of fault diagnosis in real life situations. Therefore, a Bayesian network diagnosis method based on multi-source information fusion technology is proposed to solve the fault diagnosis problems caused by uncertain factors such as the high integration and complexity of the system during the process of fault diagnosis. Taking a fault of a radar receiver as an example, we study 2 typical fault phenomena and 21 fault points. After acquiring and processing multi-source information, establishing a Bayesian network model, determining conditional probability tables (CPTs), and finally outputting the diagnosis results. The results are convincing and consistent with reality, which verifies the effectiveness of this method for fault diagnosis in radar receivers. It realizes device-level fault diagnosis, which shortens the maintenance time for radars and improves the reliability and maintainability of radars. Our results have significance as a guide for judging the fault location of radars and predicting the vulnerable components of radars.<\/jats:p>","DOI":"10.3390\/s22176396","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T02:04:32Z","timestamp":1661479472000},"page":"6396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Application of a Bayesian Network Based on Multi-Source Information Fusion in the Fault Diagnosis of a Radar Receiver"],"prefix":"10.3390","volume":"22","author":[{"given":"Boya","family":"Liu","sequence":"first","affiliation":[{"name":"Radar Faculty, Ordnance NCO Academy, Army Engineering University of PLA, Wuhan 430075, China"},{"name":"Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowen","family":"Bi","sequence":"additional","affiliation":[{"name":"Radar Faculty, Ordnance NCO Academy, Army Engineering University of PLA, Wuhan 430075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Gu","sequence":"additional","affiliation":[{"name":"Radar Faculty, Ordnance NCO Academy, Army Engineering University of PLA, Wuhan 430075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wei","sequence":"additional","affiliation":[{"name":"Radar Faculty, Ordnance NCO Academy, Army Engineering University of PLA, Wuhan 430075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baozhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","unstructured":"Liu, B. (2018). On the development of military radar and its role in modern war. Dual Use Technol. Prod., 190."},{"key":"ref_2","first-page":"29","article-title":"SAR technique and its application to geologic and seismic research","volume":"23","author":"Chen","year":"2003","journal-title":"Earthquake"},{"key":"ref_3","first-page":"187","article-title":"Research and Analysis of the Radar Receiver Noise Characteristic","volume":"31","author":"Qiang","year":"2012","journal-title":"Value Eng."},{"key":"ref_4","first-page":"84","article-title":"Analysis of Different Radar Receivers","volume":"32","author":"Zhu","year":"2010","journal-title":"Mod. Radar"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, P., and Lee, C.-M. (2019). Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method. Appl. Sci., 9.","DOI":"10.3390\/app9081696"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1177\/1077546315606602","article-title":"Industrial drive fault diagnosis through vibration analysis using wavelet transform","volume":"23","author":"Jayakumar","year":"2017","journal-title":"J. Vib. Control JVC"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108243","DOI":"10.1016\/j.apacoust.2021.108243","article-title":"Fault diagnosis and severity analysis of rolling bearings using vibration image texture enhancement and multiclass support vector machines","volume":"182","author":"Jha","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.egyr.2021.09.188","article-title":"Transformer fault diagnosis method based on improved whale optimization algorithm to optimize support vector machine","volume":"7","author":"Fan","year":"2021","journal-title":"Energy Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104815","DOI":"10.1016\/j.est.2022.104815","article-title":"Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis","volume":"53","author":"Schmid","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, Y., Liu, J., Liu, F., and Geng, Z. (2021). An intelligent moving window sparse principal component analysis-based case based reasoning for fault diagnosis: Case of the drilling process. ISA Trans.","DOI":"10.1016\/j.isatra.2021.09.016"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Naganathan, G.S., Senthilkumar, M., Aiswariya, S., Muthulakshmi, S., Santhiya Riyasen, G., and Mamtha Priyadharshini, M. (2021). Internal fault diagnosis of power transformer using artificial neural network. Mater. Today Proc.","DOI":"10.1016\/j.matpr.2021.02.206"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.measurement.2015.12.045","article-title":"Transistor level fault diagnosis in digital circuits using artificial neural network","volume":"82","author":"Kumar","year":"2016","journal-title":"Measurement"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.energy.2014.01.079","article-title":"An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system","volume":"67","author":"Shao","year":"2014","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110686","DOI":"10.1016\/j.measurement.2021.110686","article-title":"Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties","volume":"190","author":"Elsisi","year":"2022","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108771","DOI":"10.1016\/j.anucene.2021.108771","article-title":"A deep transfer learning method for system-level fault diagnosis of nuclear power plants under different power levels","volume":"166","author":"Wang","year":"2022","journal-title":"Ann. Nucl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1007\/s40435-020-00669-0","article-title":"Deep learning-based cross-sensor domain adaptation for fault diagnosis of electro-mechanical actuators","volume":"8","author":"Shahin","year":"2020","journal-title":"Int. J. Dyn. Control"},{"key":"ref_17","first-page":"1285","article-title":"A Review of Data Driven-based Incipient Fault Diagnosis","volume":"42","author":"Wen","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_18","unstructured":"Ye, S. (2011). Analysis on BITE management technology of modern air traffic control radar. Manag. Obs., 6\u20137."},{"key":"ref_19","unstructured":"Yuan, H. (2000). The Modern Design Method about the Radar Mechanical Structure. Electro-Mech. Eng., 8\u201310."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3390\/atmos5010092","article-title":"Variance of Fluctuating Radar Echoes from Thermal Noise and Randomly Distributed Scatterers","volume":"5","author":"Gabella","year":"2014","journal-title":"Atmosphere"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3724\/SP.J.1187.2011.00117","article-title":"Reasoning strategies in uncertain situations for fault diagnosis of complex device","volume":"25","author":"Lai","year":"2011","journal-title":"J. Electron. Meas. Instrum."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.3724\/SP.J.1187.2012.01096","article-title":"Data processes method of single-sensor based on maximum entropy","volume":"26","author":"Zeng","year":"2012","journal-title":"J. Electron. Meas. Instrum."},{"key":"ref_23","first-page":"1","article-title":"Design of multi-channel VHF superheterodyne receiver","volume":"42","author":"Qian","year":"2019","journal-title":"Mod. Electron. Tech."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/TVLSI.2016.2598857","article-title":"A Fully Integrated Discrete-Time Superheterodyne Receiver","volume":"25","author":"Tohidian","year":"2017","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"ref_25","first-page":"99","article-title":"Adaptive Processing of Phase Coded Waveform Echo","volume":"10","author":"Chen","year":"2012","journal-title":"Radar Sci. Technol."},{"key":"ref_26","first-page":"572","article-title":"Design of Digital Synthetic Aperture Radar Receiver","volume":"31","author":"Dong","year":"2008","journal-title":"Chin. J. Electron Devices"},{"key":"ref_27","first-page":"6","article-title":"Multisource Information Fusion: Key Issues, Research Progress and New Trends","volume":"40","author":"Chen","year":"2013","journal-title":"Comput. Sci."},{"key":"ref_28","first-page":"835","article-title":"Context-based scene recognition from visual data in smart homes: An Information Fusion approach","volume":"16","author":"Juan","year":"2011","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_29","first-page":"154","article-title":"Fault diagnosis technology of hydraulic powered support based on multilevel data fusion technology","volume":"44","author":"Li","year":"2016","journal-title":"Coal Sci. Technol."},{"key":"ref_30","first-page":"1262","article-title":"Scene classification based on feature-level and decision-level fusion","volume":"36","author":"He","year":"2016","journal-title":"J. Comput. Appl."},{"key":"ref_31","first-page":"253","article-title":"Robust human action recognition scheme based on high-level feature fusion","volume":"69","author":"Rachid","year":"2012","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Begum, S., Barua, S., and Ahmed, M.U. (2014). Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning. Sensors, 14.","DOI":"10.3390\/s140711770"},{"key":"ref_33","unstructured":"Czwartacka, A., Lukasik, W., Cholewa, J., and Jakubovvski, M. (2004, January 17\u201319). A BITE subsystem for phased array transmit antenna control. Proceedings of the 15th International Conference on Microwaves, Radar and Wireless Communications (MIKON-2004), Warsaw, Poland."},{"key":"ref_34","first-page":"117","article-title":"Application of BP Network to Fault Diagnosis of Radar Frequency Source","volume":"22","author":"Qian","year":"2008","journal-title":"J. Air Force Radar Acad."},{"key":"ref_35","first-page":"13283","article-title":"Study on multiple targets tracking algorithm based on multiple sensors","volume":"22","author":"Biao","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_36","first-page":"44","article-title":"Ship Perception Information Fusion of Electronic Chart and Radar Image Based on Deep Learning Theory","volume":"43","author":"Wang","year":"2021","journal-title":"Mod. Radar"},{"key":"ref_37","first-page":"326","article-title":"Applying Method of Multi-Source Information Fusion to Achieving Early Diagnosis of Aero-Engine Rotor Fault","volume":"27","author":"Wang","year":"2009","journal-title":"J. Northwestern Polytech. Univ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Arjun, S., Brian, S., Jeremy, S., Olivier, H., and Margaret, M. (2021). Bayesian additional evidence for decision making under small sample uncertainty. BMC Med. Res. Methodol., 21.","DOI":"10.1186\/s12874-021-01432-5"},{"key":"ref_39","first-page":"891","article-title":"Transformation of Fault Trees into Bayesian Networks Methodology for Fault Diagnosis","volume":"23","author":"Medkour","year":"2017","journal-title":"Mechanika"},{"key":"ref_40","unstructured":"Lijia, X., and Zhiliang, K. (2010, January 4\u20136). Study on Fault Diagnosis for Radar\u2032s Transmitter Based on Bayesian Network. Proceedings of the 2010 2nd International Conference on Information Science and Engineering, Hangzhou, China."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1023\/A:1010181007766","article-title":"Six-Port Receiver and Frequency Measurement for Radar Applications at 35GHz","volume":"1","author":"Diskus","year":"2000","journal-title":"Subsurf. Sens. Technol. Appl."},{"key":"ref_42","first-page":"1","article-title":"Discussion on Radar Equation","volume":"48","author":"Hou","year":"2020","journal-title":"Mod. Def. Technol."},{"key":"ref_43","unstructured":"Gustafsson, A., Alfredsson, M., Danestig, M., Malmqvist, R., and Ouacha, A. (2000, January 3\u20136). A fully integrated radar receiver front end including an active tunable band pass filter and an image rejection mixer. Proceedings of the Microwave Conference, 2000 Asia-Pacific, Sydney, NSW, Australia."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:04Z","timestamp":1760141704000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176396"],"URL":"https:\/\/doi.org\/10.3390\/s22176396","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]}}}