Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis
Abstract
:1. Introduction
2. State of the Art
Work | Sensor | Technique | Accuracy |
---|---|---|---|
Hmida et al. (2021) [4] | Vibration | Torsional model of a four cylinder crankshaft | Not checked |
Lima et al. (2021) [12] | Sound | Wavelet/fractal dimensions and ANN | 99.58% |
Firmino et al. (2021) [1] | Vibration/sound | FFT/ANN | 99.30% to vibration and 98.70% to Sound |
Du et al. (2021) [14] | Vibration | Sparse decomposition and engine finite element model | Not checked |
Gu et al. (2021) [6] | Vibration | Multivariate Empirical Decomposition Mode, and Dispersion Entropy | Not Checked |
Qin et. al. (2021) [7] | Vibration | Deep twin CNN with multi-domain inputs | 97.019% |
Du et al. (2020) [5] | Vibration | probabilistic neural network | 100% |
Zheng et al. (2019) [8] | Angular Velocity | ANN | Not verified |
Jafarian et al. (2018) [15] | Vibration | FFT, ANN, SVM, and KNN | 98% |
Chen et al. (2015) [16] | Vibration | ANN | Not verified |
Tamura et al. (2011) [11] | measurement of exhaust gas temperature | Statistical analysis of acquired curve behavior | Not verified |
Rizvi et al. (2011) [9] | angular velocity | Markov Chain | Not verified |
Hu et al. (2011) [10] | angular velocity | multivariate statistical analysis algorithm | More than 90% |
Proposed Article | Vibration (smartphone) | Density of Maxima, wavelet, FFT | 100% |
3. Fundamentals
3.1. Multiresolution Wavelet Analysis
3.2. Density of Maxima
4. Methods
5. Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Status | (msec) | (Hz) | SAC-DM (Hz) | Relative Error (%) |
---|---|---|---|---|
Healthy | 9.0 | 18.5 | 19.7 | 6.5 |
1 FP | 12.6 | 13.2 | 13.3 | 0.8 |
2 FP | 11.4 | 14.6 | 15.6 | 6.8 |
SAC-DM | Wavelet | |||||
---|---|---|---|---|---|---|
Time window (s) | 0.5 | 1 | 1.5 | 2 | 4 | 5 |
Fault detection (%) | 90.75 | 100.00 | 100.00 | 75.90 | 92.80 | 100.00 |
Fault diagnosis (%) | 78.15 | 96.60 | 100.00 | 75.90 | 92.80 | 100.00 |
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Rodrigues, N.F.; Brito, A.V.; Ramos, J.G.G.S.; Mishina, K.D.V.; Belo, F.A.; Lima Filho, A.C. Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis. Sensors 2022, 22, 5077. https://doi.org/10.3390/s22145077
Rodrigues NF, Brito AV, Ramos JGGS, Mishina KDV, Belo FA, Lima Filho AC. Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis. Sensors. 2022; 22(14):5077. https://doi.org/10.3390/s22145077
Chicago/Turabian StyleRodrigues, Nayara Formiga, Alisson V. Brito, Jorge Gabriel Gomes Souza Ramos, Koje Daniel Vasconcelos Mishina, Francisco Antonio Belo, and Abel Cavalcante Lima Filho. 2022. "Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis" Sensors 22, no. 14: 5077. https://doi.org/10.3390/s22145077
APA StyleRodrigues, N. F., Brito, A. V., Ramos, J. G. G. S., Mishina, K. D. V., Belo, F. A., & Lima Filho, A. C. (2022). Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis. Sensors, 22(14), 5077. https://doi.org/10.3390/s22145077