{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:13:24Z","timestamp":1769577204386,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"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>Internal combustion engines are among the most commonly used propulsion units for drive systems in various industries such as land transportation, maritime transportation, and power generation. Their operation involves a continuous change of technical condition as a result of not only the combustion process but also their operation under conditions of variable load or ambient impact. It is therefore important to monitor the technical condition of internal combustion engines to ensure high performance and reliability over their lifetime. The article presents the test results obtained from incorrect operation of an internal combustion engine as a result of forced failures of the ignition and injection system. On this basis, a multicriteria comparison of the signal analysis of engine block vibrations was made, after the transformation of the signal from the time domain to the frequency domain, by using the induction technique obtained from the operation of decision tree algorithms. For this purpose, the amplitude spectrum in the frequency domain, scaled to absolute values of discretization for which teaching and testing data tables were created for successive harmonics, was determined for the engine block vibration signal being tested. On the basis of the developed algorithm using decision trees, a multicriteria data table was analyzed for which a compatibility path for the analyzed engine block vibration signal is created. In this way, it is confirmed with a specified degree of effectiveness, depending on the point of operation of the engine resulting from its crankshaft speed, that there is a possibility of detecting a preset ignition or injection system malfunction in the technical condition of the internal combustion engine with a probability up to about 72%.<\/jats:p>","DOI":"10.3390\/s21072470","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T10:34:09Z","timestamp":1617359649000},"page":"2470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Use of Multicriteria Inference Method to Identify and Classify Selected Combustion Engine Malfunctions Based on Vehicle Structure Vibrations"],"prefix":"10.3390","volume":"21","author":[{"given":"Krzysztof","family":"Pra\u017cnowski","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-6624","authenticated-orcid":false,"given":"Andrzej","family":"Bieniek","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9422-6374","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Mamala","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-5998","authenticated-orcid":false,"given":"Adam","family":"Deptu\u0142a","sequence":"additional","affiliation":[{"name":"Faculty of Production Engineering and Logistic, Opole University of Technology, 45-758 Opole, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.apenergy.2011.09.040","article-title":"Cylinders diagnosis system of a 1MW internal combustion engine through vibrational signal processing using DWT technique","volume":"92","author":"Barelli","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_2","unstructured":"Grajales, J., Quintero, H., Lopez, J., Romero, C., Henao, E., Zimroz, R., Bartelmus, W., Haddar, M., and Chaari, F. (2014). Advances in Condition Monitoring of Machinery in Non-Stationary Operations, Engine Diagnosis Based on Vibration Analysis Using Diffrent Fuel Blends, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tao, J., Qin, C., Li, W., and Liu, C. (2019). Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-accuracy time\u2013frequency information of vibration signals. Sensors, 19.","DOI":"10.3390\/s19153280"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.apacoust.2018.09.002","article-title":"Fault detection of injectors in diesel engines using vibration time-frequency analysis","volume":"143","author":"Mahdavian","year":"2019","journal-title":"Appl. Acoust."},{"key":"ref_5","first-page":"191","article-title":"The diagnostic model proposition of the engine vibration signal","volume":"15","author":"Komorska","year":"2008","journal-title":"J. KONES"},{"key":"ref_6","first-page":"152","article-title":"Application of vibration signal Kalman filtering to fault diagnostics of engine exhaust valve","volume":"15","author":"Puchalski","year":"2013","journal-title":"J. Vibroeng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.applthermaleng.2017.11.138","article-title":"A new knock event definition for knock detection and control optimization","volume":"131","author":"Bares","year":"2018","journal-title":"Appl. Therm. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.promfg.2018.02.063","article-title":"Dynamics and Vibration Measurements in Engines","volume":"20","author":"Ahirrao","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_9","first-page":"2020110","article-title":"Identification and classification of selected internal combustion engine inefficiency based on vehicle structure vibrations","volume":"31","author":"Mamala","year":"2020","journal-title":"Vib. Phys. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, M., and Zhen, L. (2020). Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data. Sensors, 20.","DOI":"10.3390\/s20010006"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gong, W., Chen, H., Zhang, Z., Zhang, M., Wang, R., Guan, C., and Wang, Q. (2019). A Novel Deep Learning Method for Intelligent FaultDiagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichanel Data Fusion. Sensors, 19.","DOI":"10.3390\/s19071693"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.measurement.2014.01.018","article-title":"Misfire detection in an IC engine using vibration signal and decision tree algorithms","volume":"50","author":"Sharma","year":"2014","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1016\/j.procir.2018.03.065","article-title":"Reliability Analysis for Automobile Engines: Conditional Inference Trees","volume":"72","author":"Wang","year":"2018","journal-title":"Procedia CIRP"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0166-3615(92)90048-R","article-title":"Evaluation of vibroacoustic symptoms by 145 means of the rough sets theory","volume":"20","author":"Nowicki","year":"1992","journal-title":"Comput. Ind."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/0898-1221(92)90159-F","article-title":"Rough sets analysis of diagnostic capacity of vibroacoustic symptoms","volume":"24","author":"Nowicki","year":"1992","journal-title":"Comput. Math. Appl."},{"key":"ref_16","unstructured":"Rubin, S., Micha\u0142owski, W., and S\u0142owi\u0144ski, R. (1996). Developing an emergency room diagnostic check list using rough sets\u2014A case study of appendicitis. Simulation in the Medical Scienses, Simulation Councils."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/isaf.197","article-title":"Evaluating business credid risk by means of approach integrating decision rules and case based learning","volume":"10","author":"Stefanowski","year":"2001","journal-title":"J. Intell. Syst. Account. Financ. Manag."},{"key":"ref_18","unstructured":"Helms, M.M. (2002). Encyclopedia of Information Systems, Elsevier."},{"key":"ref_19","first-page":"239","article-title":"Multicriteria Decision Support. Review of Methods","volume":"1921","author":"Trzaskalik","year":"2014","journal-title":"Zesz. Nauk. Politech. \u015al\u0105skiej. Ser. Organ. Zarz\u0105dzanie"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Horzyk, A. (2012). Information Freedeom and Associative Artificial Intelligence. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-642-29347-4_10"},{"key":"ref_21","first-page":"582","article-title":"Knowledge Graph Construction Techniques","volume":"53","author":"Liu","year":"2016","journal-title":"J. Comput. Res. Dev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0304-3975(00)00082-7","article-title":"Game tree algorithms and solution trees","volume":"252","author":"Pijls","year":"2001","journal-title":"Theor. Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Iordanov, B. (2010). Hyper Graph DB: Ageneralized Graph Database, Springer.","DOI":"10.1007\/978-3-642-16720-1_3"},{"key":"ref_24","unstructured":"Deptu\u0142a, A. (2009). Analiza por\u00f3wnawcza optymalnych drzew logicznych w ocenie odporno\u015bci parametr\u00f3w uk\u0142adu na zmiany warunk\u00f3w pracy. XXXVIII Konferencja Zastosowa\u0144 Matematyki Zakopane, Instytut Matematyczny Polskiej Akademii Nauk."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/0890-5401(89)90010-2","article-title":"Inferring decision trees using the minimum description lenght principle","volume":"80","author":"Rivest","year":"1989","journal-title":"Inf. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1515\/ijame-2017-0002","article-title":"Inductive decision tree analysis of the validity rank of construction parameters of innovative gear pump after tooth root undercutting","volume":"22","author":"Partyka","year":"2017","journal-title":"Int. J. Appl. Mech. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"96","DOI":"10.5604\/01.3001.0012.7713","article-title":"Application of Complex Game-Tree Structures for the Hsu Graph in the Analysis of Automatic Transmission Gearboxes","volume":"18","author":"Deptula","year":"2018","journal-title":"J. Mach. Eng."},{"key":"ref_28","first-page":"17","article-title":"Application of dependence graphs and game trees for decision decomposition for machine systems","volume":"5","author":"Partyka","year":"2011","journal-title":"J. Autom. Mob. Robot. Intell. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4278","DOI":"10.1016\/j.eswa.2008.03.008","article-title":"An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network","volume":"36","author":"Liu","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1006\/mssp.1997.0102","article-title":"Time-Frequency Analysis in Gearbox Fault Detection Using the Wigner\u2013Ville Distribution and Pattern Recognition","volume":"11","author":"Staszewski","year":"1997","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural Network","volume":"2","author":"Specht","year":"1991","journal-title":"IEE Trans. Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1016\/j.egypro.2012.02.255","article-title":"A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks","volume":"17","author":"Duan","year":"2012","journal-title":"Energy Procedia"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MIM.2005.1502449","article-title":"Build better diagnostic decision trees","volume":"8","author":"Assaf","year":"2005","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MIM.2006.1664040","article-title":"Design for diagnosis using a diagnostic evaluation measure","volume":"4","author":"Assaf","year":"2006","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10549","DOI":"10.1007\/s10586-017-1109-8","article-title":"An enhanced J48 classification algorithm for the anomaly intrusion detection systems","volume":"22","author":"Aljawarneh","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"08003","DOI":"10.1051\/matecconf\/201925208003","article-title":"Analysis of loading history influence on fatigue and fracture surface parameters using the method of induction trees","volume":"252","author":"Macek","year":"2019","journal-title":"MATEC Web Conf."},{"key":"ref_37","unstructured":"Kr\u00f3lczyk, G., and Nies\u0142ony, P.K.J. (2020). Application of Bayes Classifier to Assess the State of Unbalance Wheel. Industrial Measurements in Machining IMM 2019, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:12Z","timestamp":1760363892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":37,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072470"],"URL":"https:\/\/doi.org\/10.3390\/s21072470","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,2]]}}}