{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:58:38Z","timestamp":1773727118198,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018R1D1A1A02086093"],"award-info":[{"award-number":["2018R1D1A1A02086093"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A5A8018822"],"award-info":[{"award-number":["2020R1A5A8018822"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C1013557"],"award-info":[{"award-number":["2021R1A2C1013557"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the fault classification process, filter methods that sequentially remove unnecessary features have long been studied. However, the existing filter methods do not have guidelines on which, and how many, features are needed. This study developed a multi-filter clustering fusion (MFCF) technique, to effectively and efficiently select features. In the MFCF process, a multi-filter method combining existing filter methods is first applied for feature clustering; then, key features are automatically selected. The union of key features is utilized to find all potentially important features, and an exhaustive search is used to obtain the best combination of selected features to maximize the accuracy of the classification model. In the rotating machinery examples, fault classification models using MFCF were generated to classify normal and abnormal conditions of rotational machinery. The obtained results demonstrated that classification models using MFCF provide good accuracy, efficiency, and robustness in the fault classification of rotational machinery.<\/jats:p>","DOI":"10.3390\/s22062192","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"2192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4294-788X","authenticated-orcid":false,"given":"Solichin","family":"Mochammad","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Pusan National University, Busan 46241, Korea"},{"name":"Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0342-5189","authenticated-orcid":false,"given":"Yoojeong","family":"Noh","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Pusan National University, Busan 46241, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9492-8963","authenticated-orcid":false,"given":"Young-Jin","family":"Kang","sequence":"additional","affiliation":[{"name":"Research Institute of Mechanical Technology, Pusan National University, Busan 46241, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunhwa","family":"Park","sequence":"additional","affiliation":[{"name":"H&A Research Center, LG Electronics, Changwon 51554, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jangwoo","family":"Lee","sequence":"additional","affiliation":[{"name":"H&A Research Center, LG Electronics, Changwon 51554, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Chin","sequence":"additional","affiliation":[{"name":"H&A Research Center, LG Electronics, Changwon 51554, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107351","DOI":"10.1016\/j.ymssp.2020.107351","article-title":"A noise reduction method based on adaptive weighted symplectic geometry decomposition and its application in early gear fault diagnosis","volume":"149","author":"Cheng","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106507","DOI":"10.1016\/j.knosys.2020.106507","article-title":"Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis","volume":"211","author":"Li","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_3","first-page":"259","article-title":"Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters","volume":"8","author":"Fuli","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.compind.2019.05.005","article-title":"A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis","volume":"110","author":"Li","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108392","DOI":"10.1016\/j.measurement.2020.108392","article-title":"Symplectic weighted sparse support matrix machine for gear fault diagnosis","volume":"168","author":"Li","year":"2021","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103768","DOI":"10.1016\/j.mechmachtheory.2019.103768","article-title":"Total variation on horizontal visibility graph and its application to rolling bearing fault diagnosis","volume":"147","author":"Gao","year":"2020","journal-title":"Mech. Mach. Theory"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.eswa.2018.01.041","article-title":"Hybrid feature selection using component co-occurrence based feature relevance measurement","volume":"102","author":"Youwei","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.knosys.2012.06.005","article-title":"A novel probabilistic feature selection method for text classification","volume":"36","author":"Uysal","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101224","DOI":"10.1016\/j.ecoinf.2021.101224","article-title":"An evaluation of feature selection methods for environmental data","volume":"61","author":"Dimitrios","year":"2021","journal-title":"Ecol. Inform. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Abdelhamid, N., Thabtah, F., and Abdel-Jaber, H. (2017, January 22\u201324). Phishing detection: A recent intelligent machine learning comparison based on models content and features. Proceedings of the 2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data (ISI), Beijing, China.","DOI":"10.1109\/ISI.2017.8004877"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s40745-017-0116-1","article-title":"A feature selection method based on ranked vector scores of features for classification","volume":"4","author":"Kamalov","year":"2017","journal-title":"Ann. Data Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.3233\/JIFS-169526","article-title":"Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN","volume":"34","author":"Lucero","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_14","first-page":"1","article-title":"Feature selection scheme based on Pareto method for gearbox fault diagnosis","volume":"12","author":"Ziani","year":"2017","journal-title":"Signal Process. Appl. Rotating Mach. Diagn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1016\/j.neucom.2017.11.016","article-title":"A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method","volume":"275","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hui, K., Ooi, C., Lim, M., Leong, M., and Al-Obaidi, S. (2017). An improved wrapper-based feature selection method for machinery fault diagnosis. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0189143"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1201084","DOI":"10.1155\/2019\/1201084","article-title":"Feature extraction based on adaptive multiwavelets and LTSA for rotating machinery fault diagnosis","volume":"2019","author":"Lu","year":"2019","journal-title":"Shock. Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23903","DOI":"10.3390\/s150923903","article-title":"Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal","volume":"15","author":"Cerrada","year":"2015","journal-title":"Sensors"},{"key":"ref_19","unstructured":"Huang, K., Wu, S., Li, F., Yang, C., and Gui, W. (2021). Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples. IEEE Trans. Neural Netw. Learn. Syst., 1\u201313."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1109\/TII.2021.3064377","article-title":"Universal domain adaptation in fault diagnostics with hybrid weighted deep adversarial learning","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.ins.2019.01.064","article-title":"A new hybrid ensemble feature selection framework for machine learning-based phishing detection system","volume":"484","author":"Kang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110318","DOI":"10.1016\/j.enbuild.2020.110318","article-title":"Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods","volume":"225","author":"Amir","year":"2020","journal-title":"Energy Build."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106839","DOI":"10.1016\/j.csda.2019.106839","article-title":"Benchmark for filter methods for feature selection in high-dimensional classification data","volume":"143","author":"Andrea","year":"2020","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1016\/j.eswa.2013.11.037","article-title":"Global geometric similarity scheme for feature selection in fault diagnosis","volume":"41","author":"Liu","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cortizo, J.C., and Giraldez, I. (2006, January 20\u201323). Multi criteria wrapper improvements to naive bayes learning. Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, Burgos, Spain.","DOI":"10.1007\/11875581_51"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.3233\/JIFS-161541","article-title":"Two-step based feature selection method for filtering redundant information","volume":"33","author":"Wang","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.eswa.2016.06.004","article-title":"A hybrid approach of differential evolution and artificial bee colony for feature selection","volume":"62","author":"Selma","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/S0888-3270(03)00020-7","article-title":"Gear fault detection using artificial neural network and support vector machines with genetic algorithms","volume":"12","author":"Samanta","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1016\/j.ymssp.2006.08.005","article-title":"Intelligent condition monitoring of a gearbox using artificial neural network","volume":"21","author":"Rafiee","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3785","DOI":"10.1016\/j.eswa.2008.02.026","article-title":"Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference","volume":"36","author":"Wu","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2017.02.056","article-title":"ECG beat classification via deterministic learning","volume":"240","author":"Dong","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101875","DOI":"10.1016\/j.bspc.2020.101875","article-title":"Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classier","volume":"59","author":"Jha","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"182191","DOI":"10.1016\/j.jprocont.2019.10.007","article-title":"Development of a new multi-layer perceptron based soft sensor for SO2 emissions in power plant","volume":"84","author":"Sun","year":"2019","journal-title":"J. Process Control"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ymssp.2017.03.051","article-title":"Fault diagnosis for rotary machinery with selective ensemble neural networks","volume":"113","author":"Wang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_35","unstructured":"(2021, January 01). IMS Bearings Dataset, Available online: https:\/\/ti.arc.nasa.gov\/tech\/dash\/groups\/pcoe\/prognostic-data-repository\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.engappai.2015.03.013","article-title":"Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations","volume":"42","author":"Ali","year":"2015","journal-title":"Eng. App. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Duong, B.P., Khan, S.A., Shon, D., Im, K., Park, J., Lim, D.S., Jang, B., and Kim, J.M. (2018). A reliable health indicator for fault prognosis of bearings. Sensors, 18.","DOI":"10.3390\/s18113740"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"97415","DOI":"10.1109\/ACCESS.2021.3092884","article-title":"Stable hybrid feature selection method for compressor fault diagnosis","volume":"9","author":"Mochammad","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.7734\/COSEIK.2021.34.1.25","article-title":"Fault classification model based on time domain feature extraction of vibration data","volume":"34","author":"Kim","year":"2021","journal-title":"J. Comp. Struct. Eng. Inst. Korea"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:35:04Z","timestamp":1760135704000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062192"],"URL":"https:\/\/doi.org\/10.3390\/s22062192","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,11]]}}}