Identification of Drivability Failures Using Adaline in an ECU
Keywords:
Adaline, entropy, multiresolution, transient analysis, wavelet transformAbstract
Abnormalities in fuel injection systems affect the drivability of vehicles, blemishing the driver's maneuvering experience or the smoothness of the response of these vehicles under different operating conditions. With the technological advances achieved by the automobile industry, objective methods for detecting anomalies in vehicle drivability have been studied over the last few years, with emphasis on methodologies using time-frequency analysis with wavelet transform. When performing an extraction of characteristics through wavelet decomposition aiming to detect abrupt transient variations, it becomes possible to improve the drivability of a vehicle identifying the occurrence of failures by using the energy of the decomposed signal. Therefore, using the concepts of continuous wavelet transform and entropy of information, this work makes a time-frequency analysis of the rotation signal of an internal combustion engine. The samples collected from the motor are standardized, the continuous wavelet transform is calculated and, finally, the entropy of the transformed signal is measured. Thus, the possibility of implementing an Adaline model capable of detecting the presence, or not, of abrupt changes in these signals is verified, and later, it can be embedded in an electronic control unit (ECU). The results show that the use of the Log Energy entropy as an input of the Adaline model is promising, granting 100% of accuracy on the dataset studied.
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References
C. Jauch, K. Bovee, S. Tamilarasan, L. Güvenc, and G. Rizzoni, “Modeling of the osu ecocar 2 vehicle for drivability analysis,”
IFAC-PapersOnLine, vol. 48, no. 15, pp. 300–305, 2015.
W. W. Pulkrabek, “Engineering fundamentals of the internal combustion engine,” 2004.
R. Dorey and C. Holmes, “Vehicle driveability-its characterisation and measurement,” SAE Technical Paper, Tech. Rep., 1999.
E. Cacciatori, “Evaluating the impact of driveability requirements on the performance of an energy management control architecture for a hybrid electric vehicle,” 2006.
D. F. de Arruda Santiago and R. Pederiva, “Influência da resolução tempo-freqüência da wavelet de morlet no diagnóstico de falhas de
máquinas rotativas.” Mecánica Computacional, pp. 2538–2550, 2003.
H. Zhang and W. Li, “A new method of sensor fault diagnosis based on a wavelet packet neural network for hybrid electric vehicles,” in
9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016,
pp. 1143–1147.
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.
R. Q. Marinho, H. A. Pereira, L. B. Felix, and G. A. Rodrigues, Detecç¯ao de Falhas de Dirigibilidade Veicular Através da Análise de
Tempo-Frequência de Oscilaç¯oes do Sinal de Giro de um Motor a Combust¯ao Interna. XIII SBAI - Simpósio Brasileiro de Automaç¯ao
Inteligente, 2017.
P. Schoeggl and E. Ramschak, “Vehicle driveability assessment using neural networks for development, calibration and quality tests,” SAE Technical Paper, Tech. Rep., 2000.
L. Xiang and A. Hu, “Comparison of methods for different time-frequency analysis of vibration signal.” J. Softw., vol. 7, no. 1,
pp. 68–74, 2012.
M. Ralston, M. Rauch-Davies, K. Li-Chun, X. Hui-Ping, and Y. Di-Sheng, “General method to reduce cross-term interference in the
wigner-ville decomposition,” in 2007 SEG Annual Meeting. OnePetro, 2007.
J. E. Castilho, M. Domingues, O. Mendes, and A. Pagamisse, “Introduçao ao mundo das wavelets,” Sociedade Brasileira de
matemática Aplicada e Computacional, Sao Carlos, 2012.
R. Merry and M. Steinbuch, Wavelet theory and applications: literature study. Eindhoven University of Technology, 2005.
S. Sarkar, S. Das, and P. Purkait, “Wavelet and SFAM based classification of induction motor stator winding short circuit faults and
incipient insulation failures,” in 2013 IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). IEEE, 2013, pp. 237–242.
R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on information theory, vol. 38, no. 2, pp. 713–718, 1992.
S. Raghu, N. Sriraam, and G. P. Kumar, “Effect of wavelet packet log energy entropy on electroencephalogram (eeg) signals,” International Journal of Biomedical and Clinical Engineering (IJBCE), vol. 4, no. 1, pp. 32–43, 2015.
S. Aydın, H. M. Sarao˘glu, and S. Kara, “Log energy entropy-based eeg classification with multilayer neural networks in seizure,” Annals of biomedical engineering, vol. 37, no. 12, p. 2626, 2009.
MathWorks. (2005) Wentropy, entropy (wavelet packet). [Online]. Available: https://www.mathworks.com/help/wavelet/ref/wentropy.html
I. N. Da Silva, D. H. Spatti, and R. A. Flauzino, “Redes neurais artificiais para engenharia e ciências aplicadas-curso prático,” São Paulo: Artliber, 2010.
