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Misfire Detection in Spark Ignition Engine Using Transfer Learning.
S Naveen Venkatesh1, G Chakrapani1, S Babudeva Senapti2
1School of Mechanical Engineering, VIT University Chennai Campus, Vandalur-Kelambakkam Road, Keelakottatiyur, Chennai-600127, India.
Computational Intelligence and Neuroscience
|July 18, 2022
Summary
This study introduces transfer learning for internal combustion engine misfire detection. Deep learning models analyze vibration signals to accurately identify engine misfires, improving vehicle performance and fuel efficiency.
Area of Science:
- Automotive Engineering
- Artificial Intelligence
- Signal Processing
Background:
- Undetected engine misfires lead to significant fuel and power loss, drastically reducing vehicle performance.
- Conventional misfire diagnostic techniques often require high human expertise, necessitating more intelligent and automated solutions.
- Misfire detection in internal combustion engines (IC engines) is crucial for maintaining optimal engine operation and efficiency.
Purpose of the Study:
- To propose and evaluate the use of transfer learning technology for accurate misfire detection in IC engines.
- To investigate the effectiveness of deep learning algorithms in classifying engine misfire states using vibration signal data.
- To identify the optimal pretrained deep learning network and hyperparameters for reliable misfire detection.
Main Methods:
- Vibration signals were collected from the engine head and converted into plots for input into deep learning algorithms.
- Pretrained deep learning networks, including AlexNet, VGG-16, GoogLeNet, and ResNet-50, were employed for misfire state identification.
- The impact of hyperparameters (batch size, solver, learning rate, train-test split ratio) on pretrained network performance was analyzed.
Main Results:
- Deep learning algorithms demonstrated the capability to learn from vibration signal plots and classify engine misfire states.
- Analysis identified the best-performing pretrained network for effective misfire detection.
- The study successfully validated the application of transfer learning for intelligent engine diagnostics.
Conclusions:
- Transfer learning offers a promising approach for developing intelligent and automated tools for IC engine misfire detection.
- Utilizing vibration signal analysis with deep learning can significantly improve the accuracy and efficiency of misfire diagnostics.
- The findings provide a foundation for enhanced engine monitoring systems, reducing fuel waste and improving vehicle performance.
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