{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:18:54Z","timestamp":1776093534526,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"National Centre for Research and Development in Poland","doi-asserted-by":"publisher","award":["LIDER\/3\/0005\/L-9\/17\/NCBR\/2018"],"award-info":[{"award-number":["LIDER\/3\/0005\/L-9\/17\/NCBR\/2018"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The development of a machine\u2019s condition monitoring system is often a challenging task. This process requires the collection of a sufficiently large dataset on signals from machine operation, context information related to the operation conditions, and the diagnosis experience. The two referred problems are today relatively easy to solve. The hardest to describe is the diagnosis experience because it is based on imprecise and non-numerical information. However, it is essential to process acquired data to develop a robust monitoring system. This article presents a framework for a system dedicated to recommending processing algorithms for condition monitoring. It includes a database and fuzzy-logic-based modules composed within the system. Based on the contextual knowledge provided by the user, the procedure suggests processing algorithms. This paper presents the evaluation of the proposed agent on two different parallel gearboxes. The results of the system are processing algorithms with assigned model types. The obtained results show that the algorithms recommended by the system achieve a higher accuracy than those selected arbitrarily. The results obtained allow for an average of 5 to 14.5% higher accuracy.<\/jats:p>","DOI":"10.3390\/s22103695","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"3695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4574-2580","authenticated-orcid":false,"given":"Jakub","family":"Gorski","sequence":"first","affiliation":[{"name":"Department of Robotics and Mechatronics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9350-4125","authenticated-orcid":false,"given":"Mateusz","family":"Heesch","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2555-0052","authenticated-orcid":false,"given":"Michal","family":"Dziendzikowski","sequence":"additional","affiliation":[{"name":"Airworthiness Division, Air Force Institute of Technology, ul. Ks. Boleslawa 6, 01-494 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5289-9826","authenticated-orcid":false,"given":"Ziemowit","family":"Dworakowski","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106811","DOI":"10.1016\/j.measurement.2019.07.039","article-title":"Vibration-based diagnostics of epicyclic gearboxes\u2014From classical to soft-computing methods","volume":"147","author":"Jablonski","year":"2019","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.measurement.2012.09.011","article-title":"Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems","volume":"46","author":"Jablonski","year":"2013","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_3","first-page":"1","article-title":"Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions","volume":"628","author":"Barszcz","year":"2015","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.renene.2017.06.089","article-title":"Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data","volume":"116","author":"Dao","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Mart\u00ednez-Garc\u00eda, M., Garlick, M., Latimer, A., and Cruz-Manzo, S. (2017, January 26\u201330). Condition monitoring of combustion system on industrial gas turbines based on trend and noise analysis. Proceedings of the ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, Charlotte, NC, USA.","DOI":"10.1115\/GT2017-64288"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lee, K., Jeong, S., Sim, S.H., and Shin, D.H. (2019). A novelty detection approach for tendons of prestressed concrete bridges based on a convolutional autoencoder and acceleration data. Sensors, 19.","DOI":"10.3390\/s19071633"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106495","DOI":"10.1016\/j.ymssp.2019.106495","article-title":"A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects","volume":"140","author":"Sarmadi","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"G\u00f3rski, J., Jab\u0142o\u0144ski, A., Heesch, M., Dziendzikowski, M., and Dworakowski, Z. (2021). Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults. Sensors, 21.","DOI":"10.3390\/s21103536"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.ymssp.2017.12.008","article-title":"A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy","volume":"105","author":"Li","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2019.01.038","article-title":"Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading","volume":"124","author":"Vamsi","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gligorijevic, J., Gajic, D., Brkovic, A., Savic-Gajic, I., Georgieva, O., and Di Gennaro, S. (2016). Online condition monitoring of bearings to support total productive maintenance in the packaging materials industry. Sensors, 16.","DOI":"10.3390\/s16030316"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.energy.2016.08.039","article-title":"Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery","volume":"136","author":"Brkovic","year":"2017","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3550","DOI":"10.3390\/s21103550","article-title":"Contrastive Learning for Fault Detection and Diagnostics in the Fault Types","volume":"21","author":"Rombach","year":"2021","journal-title":"Sensors"},{"key":"ref_14","first-page":"1","article-title":"Fault Diagnosis of a Rotor-Bearing System under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images","volume":"70","author":"Shao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","unstructured":"Long, J., Chen, Y., Yang, Z., Huang, Y., and Li, C. (2022). A novel self-training semi-supervised deep learning approach for machinery fault diagnosis. Int. J. Prod. Res., 1\u201314."},{"key":"ref_16","first-page":"353","article-title":"Utilizing Ontologies to Integrate Heterogeneous Decision Support Systems","volume":"1","author":"Sonnleitner","year":"2011","journal-title":"Ind. Saf. Life Cycle Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107141","DOI":"10.1016\/j.ymssp.2020.107141","article-title":"Foundations of population-based SHM, Part I: Homogeneous populations and forms","volume":"148","author":"Bull","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107144","DOI":"10.1016\/j.ymssp.2020.107144","article-title":"Foundations of Population-based SHM, Part II: Heterogeneous populations\u2014Graphs, networks, and communities","volume":"148","author":"Gosliga","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107142","DOI":"10.1016\/j.ymssp.2020.107142","article-title":"Foundations of population-based SHM, Part III: Heterogeneous populations\u2014Mapping and transfer","volume":"149","author":"Gardner","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107692","DOI":"10.1016\/j.ymssp.2021.107692","article-title":"Foundations of population-based SHM, Part IV: The geometry of spaces of structures and their feature spaces","volume":"157","author":"Tsialiamanis","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","article-title":"Recommender system application developments: A survey","volume":"74","author":"Lu","year":"2015","journal-title":"Decis. Support Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.3233\/IDA-163209","article-title":"Hybrid recommender systems: A systematic literature review","volume":"21","author":"Morisio","year":"2017","journal-title":"Intell. Data Anal."},{"key":"ref_24","first-page":"31","article-title":"Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System","volume":"110","author":"Thorat","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10462-017-9539-5","article-title":"Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning","volume":"50","author":"Tarus","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"776","DOI":"10.2991\/ijcis.2017.10.1.52","article-title":"Fuzzy tools in recommender systems: A survey","volume":"10","author":"Yera","year":"2017","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.eswa.2006.04.012","article-title":"An intelligent fuzzy-based recommendation system for consumer electronic products","volume":"33","author":"Cao","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/978-3-319-57261-1_25","article-title":"A Fuzzy Logic Based Recommendation System for Classified Advertisement Websites","volume":"573","author":"Sharif","year":"2017","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s00500-015-1826-y","article-title":"A type-2 fuzzy logic recommendation system for adaptive teaching","volume":"21","author":"Almohammadi","year":"2017","journal-title":"Soft Comput."},{"key":"ref_30","first-page":"374","article-title":"A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation","volume":"18","author":"Lee","year":"2010","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.comcom.2017.10.005","article-title":"Type-2 fuzzy ontology\u2014Aided recommendation systems for IoT\u2013based healthcare","volume":"119","author":"Ali","year":"2018","journal-title":"Comput. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/978-3-319-71078-5_26","article-title":"Fuzzy logic based personalized task recommendation system for field services","volume":"10630","author":"Mohamed","year":"2017","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.18576\/amis\/100428","article-title":"Reviews on determining the number of clusters","volume":"10","author":"Xu","year":"2016","journal-title":"Appl. Math. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jab\u0142o\u0144ski, A. (2021). Condition Monitoring Algorithms in MATLAB\u00ae, Springer. [1st ed.].","DOI":"10.1007\/978-3-030-62749-2"},{"key":"ref_35","unstructured":"Randall, R.B. (2013). Vibration-Based Condition Monitoring, Wiley."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"26241","DOI":"10.1109\/ACCESS.2018.2837621","article-title":"Preprocessing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning","volume":"6","author":"Cao","year":"2018","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3695\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:51Z","timestamp":1760137791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3695"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,12]]},"references-count":36,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103695"],"URL":"https:\/\/doi.org\/10.3390\/s22103695","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,12]]}}}