Fault Diagnosis of ME Marine Diesel Engine Fuel Injector with Novel IRCMDE Method

Shi, Qingguo; Hu, Yihuai; Yan, Guohua

doi:10.2478/pomr-2023-0043

Abstract

As an important component of the fuel injection system, the fuel injector is crucial for ensuring the power, economy, and emissions for a whole ME (machine electronically-controlled) marine diesel engine. However, injectors are most prone to failures such as reduced pressure at the opening valve, clogged spray holes and worn needle valves, because of the harsh working conditions. The failure characteristics are non-stationary and non-linear. Therefore, to efficiently extract fault features, an improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed, which uses the energy distribution of sampling points as weights for coarse-grained calculation, then fast correlation-based filter(FCBF) and support vector machine (SVM) are used for feature selection and fault classification, respectively. The experimental results from a MAN B&W 6S35ME-B9 marine diesel engine show that the proposed algorithm can achieve 92.12% fault accuracy for injector faults, which is higher than multiscale dispersion entropy (MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale permutation entropy (MPE). Moreover, the experiment has also proved that, due to the double-walled structure of the high-pressure fuel pipe, the fuel injection pressure signal is more accurate than the vibration signal in reflecting the injector operating conditions.

References

C.G. Rodriguez, M.I. Lamas, J.D. Rodriguez and A. Abbas, “Analysis of the Pre-Injection System of a Marine Diesel Engine Through Multiple-Criteria Decision-Making and Artificial Neural Networks,” Pol. Marit. Res., vol. 28, no. 4, pp. 88-96, 2022, doi: 10.2478/pomr-2021-0051.
Search in Google Scholar Back to article
F. Gao, “An integrated risk analysis method for tanker cargo handling operation using the cloud model and DEMATEL method,” Ocean Eng., vol. 266, pp. 113021, 2022, doi: 10.1016/j.oceaneng.2022.113021.
Search in Google Scholar Back to article
R. Varbanets, et al., “Concept of Vibroacoustic Diagnostics of the Fuel Injection and Electronic Cylinder Lubrication Systems of Marine Diesel Engines,” Pol. Marit. Res., vol. 29, no. 4, pp. 88-96, 2022, doi: 10.2478/pomr-2022-0046.
Search in Google Scholar Back to article
J. Kowalski, “An Experimental Study of Emission and Combustion Characteristics of Marine Diesel Engine with Fuel Injector Malfunctions,” Pol. Marit. Res., vol. 23, no. 1, pp. 77-84, 2016, doi: 10.1515/pomr-2016-0011.
Search in Google Scholar Back to article
M.G. Thurston, M.R. Sullivan and S.P. McConky, “Exhaust-gas temperature model and prognostic feature for diesel engines,” Appl. Therm. Eng., vol. 229, pp. 120578, 2023, doi: 10.1016/j.applthermaleng.2023.120578.
Search in Google Scholar Back to article
Y. Li, W. Zhou and Y. Zi, “A graphic pattern feature-mapping-based data-driven condition monitoring method for diesel engine malfunction identification and classification,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 233, no. 1, pp. 202-212, 2019, doi: 10.1177/0954406218755186.
Search in Google Scholar Back to article
M. Zhang, Y. Zi, L. Niu, S. Xi and Y. Li, “Intelligent Diagnosis of V-Type Marine Diesel Engines Based on Multifeatures Extracted From Instantaneous Crankshaft Speed,” IEEE T. Instrum. Meas., vol. 68, no. 3, pp. 722-740, 2019, doi: 10.1109/TIM.2018.2857018.
Search in Google Scholar Back to article
Y. Yang, A. Ming, Y. Zhang and Y. Zhu, “Discriminative non-negative matrix factorisation (DNMF) and its application to the fault diagnosis of diesel engine,” Mech. Syst. Signal Pr., vol. 95, pp. 158-171, 2017, doi: 10.1016/j. ymssp.2017.03.026.
Search in Google Scholar Back to article
E. Ftoutou and M. Chouchane, “Diesel engine injection faults’ detection and classification utilizing unsupervised fuzzy clustering techniques,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 233, no. 16, pp. 5622-5636, 2019, doi: 10.1177/0954406219849089.
Search in Google Scholar Back to article
S.M. Ramteke, H. Chelladurai and M. Amarnath, “Diagnosis of Liner Scuffing Fault of a Diesel Engine via Vibration and Acoustic Emission Analysis,” Journal of Vibration Engineering & Technologies, vol. 8, no. 6, pp. 815-833, 2020, doi: 10.1007/s42417-019-00180-7.
Search in Google Scholar Back to article
A. Zabihi-Hesari, S. Ansari-Rad, F.A. Shirazi and M. Ayati, “Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 233, no. 6, pp. 1910-1923, 2019, doi: 10.1177/0954406218778313.
Search in Google Scholar Back to article
L. Li, S. Tiexiong, F. Ma and Y. Pu, “Research on a small sample fault diagnosis method for a high-pressure common rail system,” Advances in Mechanical Engineering, vol. 13, no. 9, pp. 2072279549, 2021, doi: 10.1177/16878140211046103.
Search in Google Scholar Back to article
A. Zabihi-Hesari, S. Ansari-Rad, F.A. Shirazi and M. Ayati, “Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 233, no. 6, pp. 1910-1923, 2019, doi: 10.1177/0954406218778313.
Search in Google Scholar Back to article
A. Taghizadeh-Alisaraei and A. Mahdavian, “Fault detection of injectors in diesel engines using vibration time-frequency analysis,” Appl. Acoust., vol. 143, pp. 48-58, 2019, doi: 10.1016/j.apacoust.2018.09.002.
Search in Google Scholar Back to article
Y. Chen, T. Zhang, Z. Luo and K. Sun, “A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method,” Applied Sciences, vol. 9, no. 11, pp. 2356, 2019, doi: 10.3390/app9112356.
Search in Google Scholar Back to article
Y. Shang, G. Lu, Y. Kang, Z. Zhou, B. Duan and C. Zhang, “A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings,” J. Power Sources, vol. 446, pp. 227275, 2020, doi: 10.1016/j. jpowsour.2019.227275.
Search in Google Scholar Back to article
K. Zhu and H. Li, “A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 230, no. 13, pp. 2314-2322, 2016, doi: 10.1177/0954406215593568.
Search in Google Scholar Back to article
Y. Ma, J. Cheng, P. Wang, J. Wang and Y. Yang, “Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score,” Measurement, vol. 179, pp. 109495, 2021, doi: 10.1016/j. measurement.2021.109495.
Search in Google Scholar Back to article
C. Ma, Y. Li, X. Wang and Z. Cai, “Early fault diagnosis of rotating machinery based on composite zoom permutation entropy,” Reliab. Eng. Syst. Safe., vol. 230, pp. 108967, 2023, doi: 10.1016/j.ress.2022.108967.
Search in Google Scholar Back to article
S. Wu, P. Wu, C. Wu, J. Ding and C. Wang, “Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine,” Entropy-Switz., vol. 14, no. 8, pp. 1343-1356, 2012, doi: 10.3390/e14081343.
Search in Google Scholar Back to article
Y. Li, G. Li, Y. Wei, B. Liu and X. Liang, “Health condition identification of planetary gearboxes based on variational mode decomposition and generalised composite multi-scale symbolic dynamic entropy,” Isa T., vol. 81, pp. 329-341, 2018, doi: 10.1016/j.isatra.2018.06.001.
Search in Google Scholar Back to article
H. Azami and J. Escudero, “Amplitude- and Fluctuation-Based Dispersion Entropy,” Entropy-Switz., vol. 20, no. 3, pp. 210, 2018, doi: 10.3390/e20030210.
Search in Google Scholar Back to article
C. Gu, X. Qiao, H. Li and Y. Jin, “Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy,” Shock Vib., vol. 2021, pp. 1-14, 2021, doi: 10.1155/2021/9213697.
Search in Google Scholar Back to article
X. Yan and M. Jia, “Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection,” Knowl.-Based Syst., vol. 163, pp. 450-471, 2019, doi: 10.1016/j.knosys.2018.09.004.
Search in Google Scholar Back to article
Y. Ke, C. Yao, E. Song, Q. Dong and L. Yang, “An early fault diagnosis method of common-rail injector based on improved CYCBD and hierarchical fluctuation dispersion entropy,” Digit. Signal Process., vol. 114, pp. 103049, 2021, doi: 10.1016/j.dsp.2021.103049.
Search in Google Scholar Back to article
R. Dhandapani, I. Mitiche, S. McMeekin and G. Morison, “A Novel Bearing Faults Detection Method Using Generalised Gaussian Distribution Refined Composite Multiscale Dispersion Entropy,” IEEE T. Instrum. Meas., vol. 71, pp. 1-12, 2022, doi: 10.1109/TIM.2022.3187717.
Search in Google Scholar Back to article
Y. Ma, J. Cheng, P. Wang, J. Wang and Y. Yang, “Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score,” Measurement, vol. 179, pp. 109495, 2021, doi: 10.1016/j. measurement.2021.109495.
Search in Google Scholar Back to article
Y. Liu, J. Zhang and L. Ma, “A fault diagnosis approach for diesel engines based on self-adaptive WVD, improved FCBF and PECOC-RVM,” Neurocomputing, vol. 177, pp. 600-611, 2016, doi: 10.1016/j.neucom.2015.11.074.
Search in Google Scholar Back to article
M. Zhang, Y. Zi, L. Niu, S. Xi and Y. Li, “Intelligent Diagnosis of V-Type Marine Diesel Engines Based on Multifeatures Extracted From Instantaneous Crankshaft Speed,” IEEE T. Instrum. Meas., vol. 68, no. 3, pp. 722-740, 2019, doi: 10.1109/TIM.2018.2857018.
Search in Google Scholar Back to article
C. Zhao, J. Sun, S. Lin and Y. Peng, “Rolling mill bearings fault diagnosis based on improved multivariate variational mode decomposition and multivariate composite multiscale weighted permutation entropy,” Measurement, vol. 195, pp. 111190, 2022, doi: 10.1016/j.measurement.2022.111190.
Search in Google Scholar Back to article
Y. Ma, J. Cheng, P. Wang, J. Wang and Y. Yang, “Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score,” Measurement, vol. 179, pp. 109495, 2021, doi: 10.1016/j. measurement.2021.109495.
Search in Google Scholar Back to article
Y. Liu, J. Zhang and L. Ma, “A fault diagnosis approach for diesel engines based on self-adaptive WVD, improved FCBF and PECOC-RVM,” Neurocomputing, vol. 177, pp. 600-611, 2016, doi: 10.1016/j.neucom.2015.11.074.
Search in Google Scholar Back to article
B. Mei, L. Sun, G. Shi and X. Liu, “Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder,” Pol. Marit. Res., vol. 26, no. 3, pp. 115-127, 2019, doi: 10.2478/pomr-2019-0052.
Search in Google Scholar Back to article
Y. Ke, C. Yao, E. Song, Q. Dong and L. Yang, “An early fault diagnosis method of common-rail injector based on improved CYCBD and hierarchical fluctuation dispersion entropy,” Digit. Signal Process., vol. 114, pp. 103049, 2021, doi: 10.1016/j.dsp.2021.103049.
Search in Google Scholar Back to article
L. Zhang, J. Sun and C. Guo, “A Novel Multi-Objective Discrete Particle Swarm Optimisation with Elitist Perturbation for Reconfiguration of Ship Power System,” Pol. Marit. Res., vol. 24, no. s3, pp. 79-85, 2017, doi: 10.1515/pomr-2017-0108.
Search in Google Scholar Back to article

Fault Diagnosis of ME Marine Diesel Engine Fuel Injector with Novel IRCMDE Method

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