{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:29:27Z","timestamp":1768814967246,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study presents an industrial fault diagnosis system based on the cubic dynamic uncertain causality graph (cubic DUCG) used to model and diagnose industrial systems without sufficient data for model training. The system is developed based on cloud native technology. It contains two main parts, the diagnostic knowledge base and the inference method. The knowledge base was built by domain experts modularly based on professional knowledge. It represented the causality between events in the target industrial system in a visual and graphical form. During the inference, the cubic DUCG algorithm could dynamically generate the cubic causal graph according to the real-time data and perform the logic and probability calculations based on the generated cubic DUCG models, visually displaying the dynamic causal evolution of faults. To verify the system\u2019s feasibility, we rebuild a fault-diagnosis model of the secondary circuit system of No. 1 at the Ningde nuclear power plant based on the new system. Twenty-four fault cases were used to test the diagnostic accuracy of the system, and all faults were correctly diagnosed. The results showed that it was feasible to use the cubic DUCG platform for fault diagnosis.<\/jats:p>","DOI":"10.3390\/s22114118","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Industrial Fault Diagnostic System Based on a Cubic Dynamic Uncertain Causality Graph"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5333-5830","authenticated-orcid":false,"given":"Xusong","family":"Bu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}]},{"given":"Hao","family":"Nie","sequence":"additional","affiliation":[{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"}]},{"given":"Zhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"}]},{"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"},{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fernandes, M., Corchado, J.M., and Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Appl. Intell., 1\u201335.","DOI":"10.1007\/s10489-022-03344-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1049\/cim2.12047","article-title":"Knowledge transfer in fault diagnosis of rotary machines","volume":"4","author":"Liu","year":"2022","journal-title":"IET Collab. Intell. Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"739","DOI":"10.3724\/SP.J.1004.2009.00739","article-title":"Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions","volume":"35","author":"Hongm","year":"2009","journal-title":"Acta Autom. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nasser, A.R., Azar, A.T., Humaidi, A.J., Al-Mhdawi, A.K., and Ibraheem, I.K. (2021). Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. Electronics, 10.","DOI":"10.3390\/electronics10232888"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9593","DOI":"10.1021\/acs.iecr.0c01071","article-title":"Fault Diagnosis Using Novel Class-Specific Distributed Monitoring Weighted Nave Bayes: Applications to Process Industry","volume":"59","author":"He","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1109\/TIA.2011.2168800","article-title":"A New Approach for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage Source Inverters","volume":"47","author":"Estima","year":"2011","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.anucene.2016.02.024","article-title":"Fault-tree-based instantaneous risk computing core in nuclear power plant risk monitor","volume":"95","author":"Wang","year":"2016","journal-title":"Ann. Nucl. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0952-1976(93)90062-3","article-title":"Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes","volume":"6","author":"Kramer","year":"1993","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0952-1976(96)00009-7","article-title":"Fault diagnosis for industrial printers using case-based reasoning","volume":"9","author":"Grant","year":"1996","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1108\/02644400410511855","article-title":"A computer-based intelligent system for fault diagnosis of an aircraft engine","volume":"21","author":"Mustapha","year":"2004","journal-title":"Eng. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.psep.2007.10.014","article-title":"Real-time fault diagnosis using knowledge-based expert system","volume":"86","author":"Nan","year":"2008","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/37.7735","article-title":"Use of a rule-based system for process control","volume":"8","author":"Bernard","year":"1987","journal-title":"IEEE Control Syst. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MDT.1985.294719","article-title":"A Rule-Based System for Optimizing Combinational Logic","volume":"2","author":"Geus","year":"1985","journal-title":"IEEE Des. Test Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/TIE.2014.2319216","article-title":"WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM","volume":"62","author":"You","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1016\/j.compchemeng.2003.10.002","article-title":"Fault diagnosis based on Fisher discriminant analysis and support vector machines","volume":"28","author":"Chiang","year":"2004","journal-title":"Comput. Chem. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1002\/cjce.22865","article-title":"An effective fault diagnosis approach based on optimal weighted least squares support vector machine","volume":"95","author":"He","year":"2017","journal-title":"Can. J. Chem. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.3233\/JIFS-169821","article-title":"EMD and ANN based intelligent model for bearing fault diagnosis","volume":"35","author":"Malik","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/61.714488","article-title":"A combined ANN and expert system tool for transformer fault diagnosis","volume":"13","author":"Wang","year":"2000","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isatra.2019.08.012","article-title":"Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application","volume":"97","author":"Han","year":"2018","journal-title":"ISA Trans."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1007\/s10489-020-01859-1","article-title":"End-to-end CNN+LSTM deep learning approach for bearing fault diagnosis","volume":"51","author":"Khorram","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"580972","DOI":"10.1155\/2014\/580972","article-title":"Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks","volume":"2014","author":"Talebi","year":"2014","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_22","first-page":"3506012","article-title":"Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing","volume":"70","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103479","DOI":"10.1016\/j.pnucene.2020.103479","article-title":"Dynamic bayesian networks based abnormal event classifier for nuclear power plants in case of cyber security threats","volume":"128","author":"Vaddi","year":"2020","journal-title":"Prog. Nucl. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TII.2020.2988208","article-title":"Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis","volume":"17","author":"Feng","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Martin, T.P., Glasgow, J.I., F\u00e9ret, M., and Kelley, T. (1991). A Knowledge-Based System for Fault Diagnosis in Real-Time Engineering Applications, Springer.","DOI":"10.1007\/978-3-7091-7555-2_48"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1109\/TNNLS.2019.2953177","article-title":"The Cubic Dynamic Uncertain Causality Graph: A Methodology for Temporal Process Modeling and Diagnostic Logic Inference","volume":"31","author":"Dong","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11390-012-1202-7","article-title":"Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases","volume":"27","author":"Zhang","year":"2012","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/TNNLS.2015.2402162","article-title":"Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution","volume":"26","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1109\/TSMC.2015.2392711","article-title":"Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Continuous Variable, Uncertain Evidence, and Failure Forecast","volume":"45","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TR.2015.2503759","article-title":"Dynamic Uncertain Causality Graph Applied to Dynamic Fault Diagnoses and Predictions with Negative Feedbacks","volume":"65","author":"Qin","year":"2016","journal-title":"IEEE Trans. Reliab."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNNLS.2013.2279320","article-title":"Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Statistics Base, Matrix, and Application","volume":"25","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1109\/TNNLS.2017.2673243","article-title":"Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases","volume":"29","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_33","first-page":"2766","article-title":"Research on weighted logical inference for uncertain fault diagnosis","volume":"40","author":"Dong","year":"2014","journal-title":"Acta Autom. Sin."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TR.2018.2822479","article-title":"Cubic dynamic uncertain causality graph: A new methodology for modeling and reasoning about complex faults with negative feedbacks","volume":"67","author":"Dong","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_35","unstructured":"Zhao, Y. (2017). Research on DUCG Theory and Application for Fault Diagnosis and Procedure Improvement of Nuclear Power Plant. [Ph.D. Thesis, Tsinghua University]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:20:41Z","timestamp":1760138441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,28]]},"references-count":35,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22114118"],"URL":"https:\/\/doi.org\/10.3390\/s22114118","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,28]]}}}