{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:11:38Z","timestamp":1774282298741,"version":"3.50.1"},"reference-count":122,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"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>The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.<\/jats:p>","DOI":"10.3390\/s22062205","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"2205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Fault Handling in Industry 4.0: Definition, Process and Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1178-7924","authenticated-orcid":false,"given":"Heiko","family":"Webert","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]},{"given":"Tamara","family":"D\u00f6\u00df","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Natural Sciences, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9411-6781","authenticated-orcid":false,"given":"Lukas","family":"Kaupp","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]},{"given":"Stephan","family":"Simons","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Amruthnath, N., and Gupta, T. (2018, January 10\u201312). Fault class prediction in unsupervised learning using model-based clustering approach. Proceedings of the 2018 International Conference on Information and Computer Technologies (ICICT), Libertad City, Ecuador.","DOI":"10.1109\/INFOCT.2018.8356831"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Amruthnath, N., and Gupta, T. (2018, January 26\u201328). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. Proceedings of the 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore.","DOI":"10.1109\/IEA.2018.8387124"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.eswa.2016.06.035","article-title":"An evolving approach to unsupervised and Real-Time fault detection in industrial processes","volume":"63","author":"Bezerra","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Singh, M., Gehin, A.-L., and Ould-Boaumama, B. (2021). Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity. Processes, 9.","DOI":"10.3390\/pr9101801"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s11740-016-0672-9","article-title":"Development of bottleneck detection methods allowing for an effective fault repair prioritization in machining lines of the automobile industry","volume":"10","author":"Wedel","year":"2016","journal-title":"Prod. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TETCI.2016.2641452","article-title":"Model-Free Fault Detection and Isolation in Large-Scale Cyber-Physical Systems","volume":"1","author":"Alippi","year":"2017","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/10789669.2005.10391133","article-title":"Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems\u2014A Review, Part II","volume":"11","author":"Katipamula","year":"2005","journal-title":"HVAC&R Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ding, S.X. (2013). Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools, Springer.","DOI":"10.1007\/978-1-4471-4799-2"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, F., Irisarri, E., Orive, D., Marcos, M., and Estevez, E. (2015, January 8\u201311). A CPPS Architecture approach for Industry 4.0. Proceedings of the IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg.","DOI":"10.1109\/ETFA.2015.7301606"},{"key":"ref_10","unstructured":"Macintyre, J., Iliadis, L., Maglogiannis, I., and Jayne, C. Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0. Engineering Applications of Neural Networks, Proceedings of the 20th International Conference, EANN 2019, Xersonisos, Crete, Greece, 24\u201326 May 2019, Springer. Communications in Computer and Information Science, 1000."},{"key":"ref_11","unstructured":"Alur, R. (2015). Principles of Cyber-Physical Systems, MIT Press."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Boi-Ukeme, J., Ruiz-Martin, C., and Wainer, G. (2020, January 14\u201316). Real-Time Fault Detection and Diagnosis of CPS Faults in DEVS. Proceedings of the 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys), Nadi, Fiji.","DOI":"10.1109\/DependSys51298.2020.00017"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2600000007","article-title":"Sensor Fault Diagnosis","volume":"3","author":"Reppa","year":"2016","journal-title":"Found. Trends Syst. Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110638","DOI":"10.1016\/j.jss.2020.110638","article-title":"Finding faults: A scoping study of fault diagnostics for Industrial Cyber\u2013Physical Systems","volume":"168","author":"Dowdeswell","year":"2020","journal-title":"J. Syst. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/S0967-0661(97)00046-4","article-title":"Supervision, fault-detection and fault-diagnosis methods\u2014An introduction","volume":"5","author":"Isermann","year":"1997","journal-title":"Control. Eng. Pract."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","article-title":"A review of process fault detection and diagnosis: Part III: Process history based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.procs.2021.01.265","article-title":"CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory","volume":"180","author":"Kaupp","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10845-018-1433-8","article-title":"Literature review of Industry 4.0 and related technologies","volume":"31","author":"Oztemel","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1007\/s10845-018-1431-x","article-title":"Data-driven prognostic method based on self-supervised learning approaches for fault detection","volume":"31","author":"Wang","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Park, Y.-J., Fan, S.-K.S., and Hsu, C.-Y. (2020). A Review on Fault Detection and Process Diagnostics in Industrial Processes. Processes, 8.","DOI":"10.3390\/pr8091123"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Milis, G.M., Eliades, D.G., Panayiotou, C.G., and Polycarpou, M.M. (2016, January 24\u201329). A cognitive fault-detection design architecture. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727555"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yen, I., Zhang, S., Bastani, F., and Zhang, Y. (2017, January 6\u20139). A Framework for IoT-Based Monitoring and Diagnosis of Manufacturing Systems. Proceedings of the 2017 IEEE Symposium on Service-Oriented System Engineering (SOSE), San Francisco, CA, USA.","DOI":"10.1109\/SOSE.2017.26"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arrieta, A., Wang, S., Sagardui, G., and Etxeberria, L. (2016, January 16). Search-Based Test Case Selection of Cyber-Physical System Product Lines for Simulation-Based Validation. Proceedings of the 20th International Systems and Software Product Line Conference, Online.","DOI":"10.1145\/2934466.2946046"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Diedrich, A., Balzereit, K., and Niggemann, O. (2020). First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems, Springer.","DOI":"10.1007\/978-3-662-62746-4_12"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Balzereit, K., and Niggemann, O. (2020, January 10\u201312). Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories. Proceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland.","DOI":"10.1109\/ICPS48405.2020.9274707"},{"key":"ref_26","unstructured":"Balzereit, K., and Niggemann, O. (2021, January 13\u201315). Sound and Complete Reconfiguration for a Class of Hybrid Systems. Proceedings of the 32nd International Workshop on Principle of Diagnosis, Hamburg, Germany."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Z. (2017). Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods, Springer Vieweg.","DOI":"10.1007\/978-3-658-16756-1_4"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.automatica.2018.03.040","article-title":"Guaranteed model-based fault detection in cyber\u2013physical systems: A model invalidation approach","volume":"93","author":"Harirchi","year":"2018","journal-title":"Automatica"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Djelloul, I., Sari, Z., and Sidibe, I.D.B. (2018, January 10\u201313). Fault diagnosis of manufacturing systems using data mining techniques. Proceedings of the 2018 5th IEEE International Conference on Control, Decision and Information Technologies (CoDIT), Thessaloniki, Greece.","DOI":"10.1109\/CoDIT.2018.8394807"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10846-019-01098-8","article-title":"Lyapunov Theory Based Adaptive Neural Observers Design for Aircraft Sensors Fault Detection and Isolation","volume":"98","author":"Taimoor","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.isatra.2019.09.020","article-title":"Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains","volume":"99","author":"Wu","year":"2020","journal-title":"ISA Trans."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10626-020-00324-y","article-title":"Intermittent fault diagnosability of discrete event systems: An overview of automaton-based approaches","volume":"31","author":"Boussif","year":"2021","journal-title":"Discret. Event Dyn. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1109\/TII.2019.2902274","article-title":"Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches","volume":"15","author":"Iqbal","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.enbuild.2016.07.014","article-title":"A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis","volume":"128","author":"Li","year":"2016","journal-title":"Energy Build."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Calabrese, F., Regattieri, A., Bortolini, M., Gamberi, M., and Pilati, F. (2021). Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries. Appl. Sci., 11.","DOI":"10.3390\/app11083380"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.procir.2018.03.150","article-title":"Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems","volume":"72","author":"Buratti","year":"2018","journal-title":"Procedia CIRP"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.ifacol.2018.09.380","article-title":"Fault detection and classification using artificial neural networks","volume":"51","author":"Heo","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.measurement.2016.02.024","article-title":"Thermal analysis MLP neural network based fault diagnosis on worm gears","volume":"86","author":"Waqar","year":"2016","journal-title":"Measurement"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yan, W., and Zhou, J.-H. (2018, January 4\u20137). Early Fault Detection of Aircraft Components Using Flight Sensor Data. Proceedings of the 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy.","DOI":"10.1109\/ETFA.2018.8502608"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/TASE.2020.2983061","article-title":"Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing","volume":"17","author":"Fan","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, J., Chen, Y., and Sun, Z. (2017, January 22\u201325). A real-time fault detection and isolation strategy for gas sensor arrays. Proceedings of the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Turin, Italy.","DOI":"10.1109\/I2MTC.2017.7969906"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1016\/j.ifacol.2017.08.212","article-title":"Fault detection and estimation using kernel principal component analysis","volume":"50","author":"Kallas","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref_43","unstructured":"Costa, B.S.J., Bezerra, C.G., Guedes, L.A., and Angelov, P.P. (2015, January 12\u201316). Online fault detection based on Typicality and Eccentricity Data Analytics. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Angelov, P. (2014, January 9\u201312). Anomaly detection based on eccentricity analysis. Proceedings of the 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), Orlando, FL, USA.","DOI":"10.1109\/EALS.2014.7009497"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lou, C., and Li, X. (2018, January 25\u201327). Unsupervised Fault Detection Based on Laplacian Score and TEDA. Proceedings of the 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), Enshi, China.","DOI":"10.1109\/DDCLS.2018.8515956"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/TASE.2015.2487523","article-title":"Sensor Multifault Diagnosis With Improved Support Vector Machines","volume":"14","author":"Deng","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"16207","DOI":"10.1109\/ACCESS.2018.2812207","article-title":"A Fault Detection and Health Monitoring Scheme for Ship Propulsion Systems Using SVM Technique","volume":"6","author":"Zhou","year":"2018","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.knosys.2014.01.020","article-title":"Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection","volume":"59","author":"Xiao","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lv, F., Wen, C., Bao, Z., and Liu, M. (2016, January 6\u20138). Fault diagnosis based on deep learning. Proceedings of the 2016 American Control Conference (ACC), Boston, MA, USA.","DOI":"10.1109\/ACC.2016.7526751"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.neucom.2015.10.018","article-title":"An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process","volume":"174","author":"Gao","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.neucom.2015.03.082","article-title":"SVM and PCA based fault classification approaches for complicated industrial process","volume":"167","author":"Jing","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1016\/j.jprocont.2012.06.009","article-title":"A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process","volume":"22","author":"Yin","year":"2012","journal-title":"J. Process Control"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.compchemeng.2017.02.041","article-title":"A deep belief network based fault diagnosis model for complex chemical processes","volume":"107","author":"Zhang","year":"2017","journal-title":"Comput. Chem. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/5.58325","article-title":"The self-organizing map","volume":"78","author":"Kohonen","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","unstructured":"Chen, C., and Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data, University of California."},{"key":"ref_57","unstructured":"Arlot, S., and Genuer, R. (2014). Analysis of purely random forests bias. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.ygeno.2012.04.003","article-title":"Random forests for genomic data analysis","volume":"99","author":"Chen","year":"2012","journal-title":"Genomics"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s11634-015-0227-5","article-title":"Ensemble of a subset of kNN classifiers","volume":"12","author":"Gul","year":"2016","journal-title":"Adv. Data Anal. Classif."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ren, J., Lee, S.D., Chen, X., Kao, B., Cheng, R., and Cheung, D. (2009, January 6\u20139). Naive Bayes Classification of Uncertain Data. Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, Miami, FL, USA.","DOI":"10.1109\/ICDM.2009.90"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ji, Y., Yu, S., and Zhang, Y. (2011, January 20\u201322). A novel Naive Bayes model: Packaged Hidden Naive Bayes. Proceedings of the 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China.","DOI":"10.1109\/ITAIC.2011.6030379"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.patcog.2006.07.009","article-title":"Kernel PCA for novelty detection","volume":"40","author":"Hoffmann","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kangin, D., and Angelov, P. (2015, January 12\u201316). Evolving clustering, classification and regression with TEDA. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280528"},{"key":"ref_64","unstructured":"Suykens, J., and Vandewalle, J. (1999, January 10\u201316). Multiclass least squares support vector machines. Proceedings of the IJCNN\u201999, International Joint Conference on Neural Networks, Proceedings (Cat. No.99CH36339), Washington, DC, USA."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1016\/j.eswa.2010.08.083","article-title":"Rolling element bearing fault detection in industrial environments based on a k-means clustering approach","volume":"38","author":"Yiakopoulos","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Fang, H., Shi, H., Dong, Y., Fan, H., and Ren, S. (2017, January 9\u201312). Spacecraft power system fault diagnosis based on DNN. Proceedings of the 2017 Prognostics and System Health Management Conference (PHM-Harbin), Harbin, China.","DOI":"10.1109\/PHM.2017.8079271"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1016\/j.eswa.2010.07.119","article-title":"Fault diagnosis of ball bearings using machine learning methods","volume":"38","author":"Kankar","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/9517385","article-title":"A Novel Multimode Fault Classification Method Based on Deep Learning","volume":"2017","author":"Zhou","year":"2017","journal-title":"J. Control Sci. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional Neural Network Based Fault Detection for Rotating Machinery","volume":"377","author":"Janssens","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_70","first-page":"390134","article-title":"Gearbox Fault Identification and Classification with Convolutional Neural Networks","volume":"2015","author":"Chen","year":"2015","journal-title":"Shock Vib."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wu, L., Chen, X., Peng, Y., Ye, Q., and Jiao, J. (2012, January 11\u201314). Fault detection and diagnosis based on sparse representation classification (SRC). Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China.","DOI":"10.1109\/ROBIO.2012.6491087"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.3182\/20110828-6-IT-1002.02560","article-title":"Support Vector Machines for Fault Detection in Wind Turbines","volume":"44","author":"Laouti","year":"2011","journal-title":"IFAC Proc. Vol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.enbuild.2014.05.049","article-title":"ARX model based fault detection and diagnosis for chillers using support vector machines","volume":"81","author":"Yan","year":"2014","journal-title":"Energy Build."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1007\/s00170-012-4639-5","article-title":"Fault diagnosis on production systems with support vector machine and decision trees algorithms","volume":"67","author":"Demetgul","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.eswa.2005.11.031","article-title":"Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors","volume":"32","author":"Widodo","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1016\/j.ymssp.2006.10.005","article-title":"Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension","volume":"21","author":"Yang","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.ymssp.2006.01.007","article-title":"Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble","volume":"21","author":"Hu","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1007\/s12206-008-0603-6","article-title":"Random forests classifier for machine fault diagnosis","volume":"22","author":"Yang","year":"2008","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.enbuild.2016.06.017","article-title":"Fault detection and diagnosis for building cooling system with a tree-structured learning method","volume":"127","author":"Li","year":"2016","journal-title":"Energy Build."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Springer.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1016\/j.asoc.2010.04.012","article-title":"Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee\u2013Eastman process","volume":"11","author":"Eslamloueyan","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_83","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust Face Recognition via Sparse Representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zhao, W., Chellappa, R., and Krishnaswamy, A. (1998, January 14\u201316). Discriminant analysis of principal components for face recognition. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan.","DOI":"10.1007\/978-3-642-72201-1_4"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1108\/IJPPM-07-2017-0168","article-title":"Machine criticality based maintenance prioritization: Identifying productivity improvement potential","volume":"67","author":"Gopalakrishnan","year":"2018","journal-title":"Int. J. Product. Perform. Manag."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"35743","DOI":"10.1109\/ACCESS.2018.2850910","article-title":"Emotion Based Automated Priority Prediction for Bug Reports","volume":"6","author":"Umer","year":"2018","journal-title":"IEEE Access"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Sharma, M., Bedi, P., Chaturvedi, K., and Singh, V. (2012, January 27\u201329). Predicting the priority of a reported bug using machine learning techniques and cross project validation. Proceedings of the 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), Kochi, India.","DOI":"10.1109\/ISDA.2012.6416595"},{"key":"ref_89","first-page":"29","article-title":"A Deep-Learning-Based Bug Priority Prediction Using RNN-LSTM Neural Networks","volume":"15","author":"Sallam","year":"2021","journal-title":"E-Inform. Softw. Eng. J."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"46846","DOI":"10.1109\/ACCESS.2019.2909746","article-title":"Deep Neural Network-Based Severity Prediction of Bug Reports","volume":"7","author":"Ramay","year":"2019","journal-title":"IEEE Access"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1007\/s00170-016-9019-0","article-title":"Identifying and managing failures in stone processing industry using cost-based FMEA","volume":"88","author":"Rezaee","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Oliveira, J., Carvalho, G., Cabral, B., and Bernardino, J. (2020). Failure Mode and Effect Analysis for Cyber-Physical Systems. Futur. Internet, 12.","DOI":"10.3390\/fi12110205"},{"key":"ref_93","first-page":"1","article-title":"Failure mode and effect analysis (FMEA) implementation: A literature review","volume":"5","author":"Sharma","year":"2018","journal-title":"J. Adv. Res. Aeronaut. Space Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s00170-020-06425-0","article-title":"Literature review and prospect of the development and application of FMEA in manufacturing industry","volume":"112","author":"Wu","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/j.cie.2019.06.055","article-title":"Failure mode and effect analysis using multi-criteria decision making methods: A systematic literature review","volume":"135","author":"Liu","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"106885","DOI":"10.1016\/j.ress.2020.106885","article-title":"Failure mode and effect analysis improvement: A systematic literature review and future research agenda","volume":"199","author":"Huang","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.procs.2016.09.290","article-title":"Topology-based Safety Analysis for Safety Critical CPS","volume":"95","author":"Choley","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Biffl, S., L\u00fcder, A., Meixner, K., Rinker, F., Eckhart, M., and Winkler, D. (2021, January 8\u201310). Multi-view-Model Risk Assessment in Cyber-Physical Production Systems Engineering. Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development\u2014MODELSWARD, Online.","DOI":"10.5220\/0010224801630170"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1007\/s11668-019-00717-8","article-title":"Failure Mode Identification and Prioritization Using FMECA: A Study on Computer Numerical Control Lathe for Predictive Maintenance","volume":"19","author":"Thoppil","year":"2019","journal-title":"J. Fail. Anal. Prev."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1016\/j.eswa.2012.08.010","article-title":"Risk evaluation approaches in failure mode and effects analysis: A literature review","volume":"40","author":"Liu","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_101","first-page":"2546","article-title":"Evaluation of Safety Risks in Construction Using Fuzzy Failure Mode and Effect Analysis (FFMEA)","volume":"23","author":"Ardeshir","year":"2016","journal-title":"Sci. Iran."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2015.10.039","article-title":"Generalized multi-attribute failure mode analysis","volume":"175","author":"Chang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"92398","DOI":"10.1109\/ACCESS.2019.2928120","article-title":"A Critical Comparison of Alternative Risk Priority Numbers in Failure Modes, Effects, and Criticality Analysis","volume":"7","author":"Ciani","year":"2019","journal-title":"IEEE Access"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1080\/14783363.2017.1337506","article-title":"Failure mode and effect analysis with extended grey relational analysis method in cloud setting","volume":"30","author":"Liu","year":"2019","journal-title":"Total Qual. Manag. Bus. Excel."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Nguyen, T.-L., Shu, M.-H., and Hsu, B.-M. (2016). Extended FMEA for Sustainable Manufacturing: An Empirical Study in the Non-Woven Fabrics Industry. Sustainability, 8.","DOI":"10.3390\/su8090939"},{"key":"ref_106","first-page":"76","article-title":"Modifying risk priority number in failure modes and effects analysis","volume":"3","author":"Nguyen","year":"2016","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4512383","DOI":"10.1155\/2016\/4512383","article-title":"A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory","volume":"2016","author":"Zhou","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_108","first-page":"1384","article-title":"Total efficient risk priority number (TERPN): A new method for risk assessment","volume":"21","author":"Silvestri","year":"2016","journal-title":"J. Risk Res."},{"key":"ref_109","unstructured":"Kmenta, S., and Ishii, K. (2000, January 10\u201313). Scenario-Based FMEA: A Life Cycle Cost Perspective. Proceedings of the ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Baltimore, MD, USA."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Li, Y., Kang, R., Ma, L., and Li, L. (2011, January 12\u201315). Application and improvement study on FMEA in the process of military equipment maintenance. Proceedings of the 2011 9th International Conference on Reliability, Maintainability and Safety, Guiyang, China.","DOI":"10.1109\/ICRMS.2011.5979402"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s40092-015-0113-y","article-title":"Risk management in medical product development process using traditional FMEA and fuzzy linguistic approach: A case study","volume":"11","author":"Kirkire","year":"2015","journal-title":"J. Ind. Eng. Int."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/TR.2010.2103672","article-title":"Criticality Assessment Models for Failure Mode Effects and Criticality Analysis Using Fuzzy Logic","volume":"60","author":"Gargama","year":"2011","journal-title":"IEEE Trans. Reliab."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1504\/IJMCDM.2017.085154","article-title":"Failure mode and effects analysis using a fuzzy-TOPSIS method: A case study of subsea control module","volume":"7","author":"Kolios","year":"2017","journal-title":"Int. J. Multicriteria Decis. Mak."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ress.2017.09.017","article-title":"A combined multi-criteria approach to support FMECA analyses: A real-world case","volume":"169","author":"Carpitella","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"15733","DOI":"10.1007\/s00500-020-04903-x","article-title":"A hybrid MCDM-based FMEA model for identification of critical failure modes in manufacturing","volume":"24","author":"Lo","year":"2020","journal-title":"Soft Comput."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.cie.2016.11.003","article-title":"Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments","volume":"102","author":"Wang","year":"2016","journal-title":"Comput. Ind. Eng."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.eswa.2011.06.044","article-title":"Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP","volume":"39","author":"Kutlu","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Apriliana, A.F., Sarno, R., and Effendi, Y.A. (2018, January 6\u20137). Risk analysis of IT applications using FMEA and AHP SAW method with COBIT 5. Proceedings of the 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICOIACT.2018.8350708"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s00500-014-1321-x","article-title":"Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach","volume":"19","author":"Liu","year":"2015","journal-title":"Soft Comput."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.autcon.2014.05.013","article-title":"A review of application of multi-criteria decision making methods in construction","volume":"45","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jprocont.2020.05.015","article-title":"Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder","volume":"92","author":"Wang","year":"2020","journal-title":"J. Process Control"},{"key":"ref_122","first-page":"1526","article-title":"Nuclear Power Plant Thermocouple Sensor Fault Detection and Classification using Deep Learning and Generalized Likelihood Ratio Test","volume":"64","author":"Mandal","year":"2017","journal-title":"IEEE Trans. Nucl. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:35:26Z","timestamp":1760135726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,12]]},"references-count":122,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062205"],"URL":"https:\/\/doi.org\/10.3390\/s22062205","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,12]]}}}