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Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques
- Yixin Huangfu - McMaster University, Department of Mechanical Engineering, Canada ,
- Essam Seddik - Arab Academy for Science and Technology and Maritime Transport, Egypt ,
- Saeid Habibi - McMaster University, Department of Mechanical Engineering, Canada ,
- Alan Wassyng - McMaster University, Department of Computing and Software, Canada ,
- Jimi Tjong - McMaster University, Department of Mechanical Engineering, Canada
Journal Article
03-15-04-0027
ISSN: 1946-3936, e-ISSN: 1946-3944
Sector:
Topic:
Citation:
Huangfu, Y., Seddik, E., Habibi, S., Wassyng, A. et al., "Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques," SAE Int. J. Engines 15(4):515-525, 2022, https://doi.org/10.4271/03-15-04-0027.
Language:
English
Abstract:
Fault Detection and Diagnosis (FDD) is playing an increasingly important role in
the automotive sector as it moves toward Advanced Technology Vehicles. Reducing
the cost of sensory equipment to detect faults in Internal Combustion Engines
(ICEs) has always been a common desire for automotive researchers. This article
offers an Artificial Intelligence approach for detecting engine combustion
faults related to spark plugs using existing sensors. The study investigates two
deep learning models that are capable of learning different fault conditions
from historical sensory data. The two customized models, one Long Short-Term
Memory (LSTM) neural network and one Convolutional Neural Networks (CNN) model,
are proposed to tackle this task. The LSTM model processes the filtered sensor
data in time series, while the CNN model uses the frequency map that is novel in
the learning-based engine diagnosis field. A comprehensive engine fault dataset
is collected and includes a variety of operating conditions in relation to
engine speed, engine load, and test time. Evaluation results using this dataset
show successful detection of the fault conditions with high accuracy. In the
meantime, the results also reveal some unstable performance outside of given
operating conditions.