|Table of Contents|

Citation:
　Victor Nicodemos Guerra,Brenno Moura Castro,Dionísio Henrique Carvalho de Sá Só Martins,et al.Framework for Single Misfire Identification in a Marine Diesel Engine using Machine Learning[J].Journal of Marine Science and Application,2025,(5):1086-1102.[doi:10.1007/s11804-025-00752-y]
Click and Copy

Framework for Single Misfire Identification in a Marine Diesel Engine using Machine Learning

Info

Title:
Framework for Single Misfire Identification in a Marine Diesel Engine using Machine Learning
Author(s):
Victor Nicodemos Guerra1;  Brenno Moura Castro1;  Dionísio Henrique Carvalho de Sá Só Martins1;  Ricardo Homero Ramírez Gutiérrez2;  Ulisses Admar Barbosa Vicente Monteiro1
Affilations:
Author(s):
Victor Nicodemos Guerra1;  Brenno Moura Castro1;  Dionísio Henrique Carvalho de Sá Só Martins1;  Ricardo Homero Ramírez Gutiérrez2;  Ulisses Admar Barbosa Vicente Monteiro1
1. Federal University of Rio de Janeiro (UFRJ), Ocean Engineering Program (PENO), Centro de Tecnologia, Bloco I-108, Cidade Universitária, Ilha do Fundão ZIP 20945-970, Rio de Janeiro/RJ, Brazil;
2. State University of Amazonas, Naval Engineering Department, Escola Superior de Tecnologia, Parque Dez de Novembro, 69050-020, Manaus/AM, Brazil
Keywords:
Misfire fault|Vibration|Marine diesel engine|K-NN|SVM|Random forest
分类号:
-
DOI:
10.1007/s11804-025-00752-y
Abstract:
Misfire is a common fault in compression ignition engines, characterized by the absence or flame loss due to insufficient fuel in the cylinders. This fault is difficult to diagnose and resolve due to its multiple potential causes. This study focuses on identifying misfires in a 12-cylinder Ⅴ-type marine diesel engine by analyzing vibration data collected from 15 accelerometers mounted on the engine block. Three machine learning algorithms—K-Nearest Neighbors (K-NNs), support vector machines (SVMs), and random forests (RFs)—were employed to classify engine conditions using 18 time-domain features. Results showed that the K-NN, SVM and RF algorithms achieved F1 scores of 99.87%, 100%, and 99.87%, respectively, when using 18 time-domain features and all 15 accelerometers mounted on the engine block. Additionally, the study evaluated classification performance while reducing the number of accelerometers and features using two methods: Relief-F and general combinatory analysis (GCA). Although the GCA method yields better results when using only two accelerometers and nine features for misfire classification, its overall process required substantially more computational time compared to Relief-F. The best result obtained with Relief-F was achieved using 3 accelerometers and 18 features. Therefore, Relief-F proved to be more practical and take less overall computational time within the proposed framework.

References:

[1] Abu Alfeilat HA, Hassanat AB, Lasassmeh O, Tarawneh AS, Alhasanat MB, Eyal Salman HS, Prasath VS (2019) Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big Data 7(4): 221-248. https://doi.org/10.1089/big.2018.0175
[2] Blaine BE (2018) Winsorizing. The sage encyclopedia of educational research, measurement, and evaluation: 1817-1818. https://doi.org/10.4135/9781506326139.n747
[3] Boateng EY, Otoo J, Abase DA (2020) Basic tenets of classification algorithms k-nearest-neighbor, support vector machine, random forest and neural network: a review. Journal of Data Analysis and Information Processing 8(4): 341-357. https://doi.org/10.4236/jdaip.2020.84020
[4] Boisberranger J, Estève L, Fan TJ, Gramfort A, Grisel O (2023) Support Vector Machines. In: Scikit-learn. Available via dialog. https://scikit-learn.org/stable/modules/svm.html. Accessed 3 Aug 2023
[5] Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2013) A review of feature selection methods on synthetic data. Knowledge and Information Systems 34: 483-519. https://doi.org/10.1007/s10115-012-0487-8
[6] Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In Proceedings of The Fifth Annual Workshop on Computational Learning Theory: 144-152. https://doi.org/10.1145/130385.130401
[7] Breiman L (2001) Random forests. Machine learning 45(1): 5-32. https://doi.org/10.1023/A:1010933404324
[8] Breiman L (1996) Bagging predictors. Machine learning 24: 123-140. https://doi.org/10.1007/BF00058655
[9] Charles P, Sinha JK, Gu F, Lidstone L, Ball AD (2009) Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis. Journal of Sound and Vibration 321(3-5): 1171-1185. https://doi.org/10.1016/j.jsv.2008.10.024
[10] Chen SK, Mandal A, Chien LC, Ortiz-Soto E (2018) Machine learning for misfire detection in a dynamic skip fire engine. Sae International Journal of Engines 11(6): 965-976. https://www.jstor.org/stable/26649141
[11] Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20: 273-297. https://doi.org/10.1007/BF00994018
[12] Cui Y, Liu H, Wang Q, Zheng Z, Wang H, Yue Z, Yao M (2022a) Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation (BP) neural network. Combustion And Flame 237: 111852. https://doi.org/10.1016/j.combustflame.2021.111852
[13] Cui Y, Liu H, Wen M, Feng L, Ming Z, Zheng Z, Yao M (2022b) Optical diagnostics of misfire in partially premixed combustion under low load conditions. Fuel 329: 125432. https://doi.org/10.1016/j.fuel.2022.125432
[14] Cutler A, Cutler DR, Stevens JR (2012) Random forests. Ensemble Machine Learning: 157-175. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9326-7_5
[15] De Amorim LB, Cavalcanti GD, Cruz RM (2023) The choice of scaling technique matters for classification performance. Applied Soft Computing 133: 109924. https://doi.org/10.1016/j.asoc.2022.109924
[16] Devasenapati SB, Sugumaran V, Ramachandran KI (2010) Misfire identification in a four-stroke four-cylinder petrol engine using decision tree. Expert Systems with Applications 37(3): 2150-2160. https://doi.org/10.1016/j.eswa.2009.07.061
[17] Firmino JL, Neto JM, Oliveira AG, Silva JC, Mishina KV, Rodrigues MC (2021) Misfire detection of an internal combustion engine based on vibration and acoustic analysis. Journal of The Brazilian Society of Mechanical Sciences and Engineering 43(7): 336. https://doi.org/10.1007/s40430-021-03052-y
[18] Fratello M, Tagliaferri R (2018) Decision trees and random forests. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics: 374-383. https://doi.org/10.1016/B978-0-12-809633-8.20337-3
[19] Gholamy A, Kreinovich V, Kosheleva O (2018) Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. https://doi.org/10.6148/IJITAS.201806_11(2).0003
[20] Hassanat AB, Tarawneh AS, Abed SS, Altarawneh GA, Alrashidi M, Agamid M (2022) Rdpvr: Random data partitioning with voting rule for machine learning from class-imbalanced datasets. Electronics 11(2): 228. https://doi.org/10.3390/electronics11020228
[21] Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction, Vol. 2: 1-758. New York: Springer. https://doi.org/10.1007/978-0-387-21606-5
[22] ISO 10816-6 (1995) Mechanical vibration-Evaluation of machine vibration by measurements on non-rotating parts-Part 6: Reciprocating machines with power ratings above 100 Kw
[23] Jafarian K, Mobin M, Jafari-Marandi R, Rabiei E (2018) Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring. Measurement 128: 527-536. https://doi.org/10.1016/j.measurement.2018.04.062
[24] Jovi? A, Brki? K, Bogunovi? N (2015) A review of feature selection methods with applications. In 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO): 1200-1205. IEEE. https://doi.org/10.1109/MIPRO.2015.7160458
[25] Kononenko I (1994) Estimating attributes: Analysis and extensions of RELIEF. In European conference on machine learning: 171-182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_57
[26] Kramer O (2013) Dimensionality reduction with unsupervised nearest neighbors Vol. 51: 13-23. Berlin: Springer. https://doi.org/10.1007/978-3-642-38652-7
[27] Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK (2020) Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems And Signal Processing 138: 106587. https://doi.org/10.1016/j.ymssp.2019.106587
[28] Lujan JM, Bermudez V, Guardiola C, Abbad A (2010) A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal. Mechanical Systems and Signal Processing 24(7): 2261-2275. https://doi.org/10.1016/j.ymssp.2009.12.012
[29] Mammone A, Turchi M, Cristianini N (2009) Support vector machines. Wiley Interdisciplinary Reviews: Computational Statistics 1(3): 283-289. https://doi.org/10.1002/wics.49
[30] Martins DHCSS, De Lima AA, Pinto MF, Hemerly DO, Prego TM, Silva FL, Tarrataca L, Monteiro UA, Gutiérrez RHR, Haddad DB (2023) Hybrid data augmentation method for combined failure recognition in rotating machines. Journal of Intelligent Manufacturing 34: 1795-1813. https://doi.org/10.1007/s10845-021-01873-1
[31] Maurya RK (2019) Reciprocating engine combustion diagnostics. IN-Cylinder Pressure Measurement and Analysis. Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-11954-6
[32] Merhige R (2016) Engine misfire largely to blame for vibration onboard. In: Triton. Available via dialog. https://www.the-triton.com/2016/09/engine-misfire-largely-to-blame-for-vibration-onboard/. Accessed 20 Dec 2022
[33] MTU Detroit Diesel (2016) Surface Mining 12V4000-T1237K11. Detroit: MTU Detroit Diesel, 20p
[34] Nazarenko E, Varkentin V, Polyakova T (2019) Features of application of machine learning methods for classification of network traffic (features, advantages, disadvantages). In 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon): 1-5. IEEE. https://doi.org/10.1109/FarEastCon.2019.8934236
[35] Noble WS (2006) What is a support vector machine? Nature biotechnology 24(12): 1565-1567. https://doi.org/10.1038/nbt1206-1565
[36] Phyu TZ, Oo NN (2016) Performance comparison of feature selection methods. In MATEC web of conferences Vol. 42: 126, 521-535. https://doi.org/10.1051/matecconf/20164206002
[37] Robnik-?ikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53: 23-69. https://doi.org/10.1023/A:1025667309714
[38] Sharma A, Sugumaran V, Devasenapati SB (2014) Misfire detection in an IC engine using vibration signal and decision tree algorithms. Measurement 50: 370-380. https://doi.org/10.1016/J.MEASUREMENT.2014.01.018
[39] Singh D, Singh B (2022) Feature wise normalization: An effective way of normalizing data. Pattern Recognition 122: 108307. https://doi.org/10.1016/j.patcog.2021.108307
[40] Tamura M, Saito H, Murata Y, Kokubu K, Morimoto S (2011) Misfire detection on internal combustion engines using exhaust gas temperature with low sampling rate. Applied Thermal Engineering 31(17-18): 4125-4131. https://doi.org/10.1016/j.applthermaleng.2011.08.026
[41] Tao J, Qin C, Li W, Liu C (2019) Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-accuracy time-frequency information of vibration signals. Sensors 19(15): 3280. https://doi.org/10.3390/s19153280
[42] Taunk K, De S, Verma S, Swetapadma A (2019) A brief review of nearest neighbor algorithm for learning and classification. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS): 1255-1260. IEEE. https://doi.org/10.1109/ICCS45141.2019.9065747
[43] Ukil A (2007) Intelligent systems and signal processing in power engineering. Springer Science & Business Media. https://doi.org/10.1007/978-3-540-73170-2
[44] Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH (2018) Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics 85: 189-203. https://doi.org/10.1016/j.jbi.2018.07.014
[45] Vakharia V, Gupta VK, Kankar PK (2017) Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier. Journal of The Brazilian Society of Mechanical Sciences and Engineering 39(8): 2969-2982. https://doi.org/10.1007/s40430-017-0717-9
[46] Verikas A, Gelzinis A, Bacauskiene M (2011) Mining data with random forests: A survey and results of new tests. Pattern Recognition 44(2): 330-349. https://doi.org/10.1016/j.patcog.2010.08.011
[47] Wang C, Yue Z, Zhao Y, Ye Y, Liu X, Liu H (2023). Numerical simulation of the high-boosting influence on mixing, combustion and emissions of high-power-density engine. Journal of Thermal Science 32(3): 933-946. https://doi.org/10.1007/s11630-023-1796-9
[48] Xi W, Li Z, Tian Z, Duan Z (2018) A feature extraction and visualization method for fault detection of marine diesel engines. Measurement 116: 429-437. https://doi.org/10.1016/j.measurement.2017.11.035
[49] Zhang M, Zi Y, Niu L, Xi S, Li Y (2018) Intelligent diagnosis of Ⅴ-type marine diesel engines based on multifeatures extracted from instantaneous crankshaft speed. IEEE Transactions on Instrumentation and Measurement 68(3): 722-740. https://doi.org/10.1109/TIM.2018.2857018
[50] Zhang P, Gao W, Li Y, Wang Y (2021) Misfire detection of diesel engine based on convolutional neural networks. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235(8): 2148-2165. https://doi.org/10.1177/0954407020987077
[51] Zhang Z, Liu H, Yue Z, Li Y, Liang H, Kong X, Yao M (2023) Effects of intake high-pressure compressed air on thermal-work conversion in a stationary diesel engine. International Journal of Green Energy 20(3): 338-351. https://doi.org/10.1080/15435075.2022.2040509
[52] Zheng T, Zhang Y, Li Y, Shi L (2019) Real-time combustion torque estimation and dynamic misfire fault diagnosis in gasoline engine. Mechanical Systems and Signal Processing 126: 521-535. https://doi.org/10.1016/j.ymssp.2019.02.048
[53] Zhou H, Zhang J, Zhou Y, Guo X, Ma Y (2021) A feature selection algorithm of decision tree based on feature weight. Expert Systems with Applications 164, 113842. https://doi.org/10.1016/j.eswa.2020.113842

Memo

Memo:
Received date:2023-12-12;Accepted date:2024-9-14。<br>Corresponding author:Victor Nicodemos Guerra,E-mail:victornguerra@oceanica.ufrj.br
Last Update: 2025-10-24